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Absolute Coordinates Make Motion Generation Easy

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Absolute joint coordinates in global space, trained with velocity prediction and adaptive normalization in a simple Transformer, are sufficient to beat the local-relative motion representation on text-to-motion generation.

desk verdict A well-run empirical study that makes a plausible case for absolute coordinates, but the headline claim overreaches: the decisive comparison—local-relative trained with the full recipe—is missing. read the letter →

arxiv 2505.19377 v2 pith:NNHQWLG6 submitted 2025-05-26 cs.CV

Zichong Meng , Zeyu Han , Xiaogang Peng , Yiming Xie , Huaizu Jiang This is my paper · ORCID
classification cs.CV
keywords text-to-motiongenerationmotionrepresentationabsolutejointcoordinatesdiffusionmodelshumansynthesiscontrolSMPL-HmeshTransformer
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Text-to-motion generation has converged on a local-relative, kinematic-aware encoding that stores joint positions relative to the pelvis and previous frame, with extra features such as foot contacts. This paper argues that the redundancy is not just unnecessary but harmful for diffusion models, and that the long-abandoned alternative of absolute joint coordinates in global space is simpler and stronger. Using a plain Transformer with velocity prediction and adaptive layer normalization conditioning, without kinematic-aware auxiliary losses, the proposed model reaches state-of-the-art fidelity and text alignment, and its smallest variant already beats prior systems. The same formulation also makes trajectory control and spatial editing straightforward, without classifier guidance from control signals or input optimization, and extends naturally to generating SMPL-H mesh vertices directly from text. If correct, the paper shifts the field's default representation from engineered kinematic features back to raw coordinates.

What carries the argument

The workhorse is the absolute-coordinate motion representation: per frame, joint positions $X_i \in \mathbb{R}^{N_j \times 3}$ in global space, with no pelvis-relative or previous-frame encoding and no redundant kinematic features. The model tokenizes this 2D motion field with a convolutional patchifier that preserves temporal resolution, feeds the tokens into a Transformer with rotary position embeddings, QK normalization, SwiGLU, and adaptive layer normalization (AdaLN) for text and timestep conditioning, and trains with the flow-matching velocity objective $v$ instead of predicting $x_0$ or noise. An optional causal autoencoder compresses motion into a latent space that improves fidelity. For control, a ControlNet branch takes the same noised absolute inputs plus control signals and injects additive residuals into the frozen main backbone; for meshes, a fully convolutional mesh autoencoder compresses SMPL-H vertices to 28 latent channels and the same Transformer operates on those latents.

What would settle it

Train the same Transformer architecture, with the same AdaLN conditioning, velocity prediction, causal latent autoencoder, and identical model sizes, on the standard local-relative representation; evaluate both models with the original benchmark's official evaluation code. If the local-relative model matches or exceeds the absolute-coordinate model, the central claim is not supported.

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Extended reading notes

Core claim

The paper's central claim is that the dominant local-relative motion representation—which encodes intra-frame kinematics and inter-frame transitions around the pelvis and includes redundant non-animatable features—is not what makes modern text-to-motion diffusion models work. With absolute 3D joint coordinates as the only motion signal, a simple Transformer trained with the velocity-prediction objective, adaptive layer normalization conditioning, and a causal latent motion autoencoder achieves higher motion fidelity and better text alignment than existing systems, and it scales well as model size grows and token patch size shrinks. The paper further claims that this representation is the reason control and editing become straightforward: because outputs are already in global space, user-provided absolute trajectories or partial-body constraints can be injected directly through a frozen-backbone ControlNet without classifier guidance from control signals or inference-time optimization. Finally, the same model, with a mesh autoencoder for spatial compression, can generate SMPL-H mesh vertices from text directly, bypassing joint-to-mesh fitting.

Load-bearing premise

If the evaluation protocol used to compare methods is not a fair stand-in for the original benchmark, or if the comparison local-relative model is trained with a weaker recipe, the claim that absolute coordinates cause the improvement would collapse.

Editorial extensions

If this is right

  • Absolute coordinates remove the need for relative-to-absolute post-processing, so trajectory control and spatial editing can be done by directly feeding user-specified joint positions as conditions.
  • Because control signals are injected as additive residuals into a frozen backbone, generation runs without classifier guidance from control signals, reducing control inference time from roughly 81 seconds to about 2.5 seconds per sample.
  • The same framework generalizes to direct SMPL-H mesh vertex generation, so text-to-motion can skip SMPL fitting and the errors it accumulates.
  • Scaling model depth, width, and spatial patch granularity consistently improves quality, indicating that absolute coordinates give a representation that benefits from larger models.
  • The formulation also works in autoregressive diffusion variants, so the benefit is not tied to a single diffusion scheduling or backbone choice.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: if absolute coordinates remain competitive under the original benchmark's evaluation pipeline, the local-relative representation's remaining justification is historical, and new text-to-motion work should default to global coordinates.
  • Editorial inference: the same recipe could transfer to other structured-sequence generation tasks whose outputs are point sets or trajectories in a fixed global frame, such as robot motion or multi-agent coordination.
  • Editorial inference: because absolute coordinates expose global position natively, physics constraints, foot-contact regularization, or scene collision terms could be added directly on generated output, which the redundant representation makes awkward.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes ACMDM, a text-to-motion diffusion framework built on absolute global joint coordinates instead of the local-relative, kinematic-aware representation popularized by HumanML3D. The authors argue that this simpler representation, combined with a DiT-style Transformer, velocity prediction, AdaLN conditioning, and an optional causal latent autoencoder, achieves state-of-the-art generation quality, naturally supports trajectory control and spatial/temporal editing, and extends directly to SMPL-H mesh vertex generation. The headline results on HumanML3D are FID 0.058 and R-Precision Top-1 0.522 for ACMDM-XL-PS2, compared with FID 0.114 and R-Precision Top-1 0.500 for MARDM-v, all measured under the evaluation framework of [66].

Significance. If the central claim is fully substantiated, this paper would challenge a widely adopted design choice in text-to-motion generation and would open a simpler path to controllable generation and direct mesh-level synthesis. The paper's strengths include a systematic ablation of conditioning mechanisms, diffusion objectives, autoencoder variants, and model scaling; a clear demonstration of controllable generation without classifier guidance; and exploratory results on direct SMPL-H mesh generation, KIT-ML, and autoregressive diffusion variants. These are useful empirical contributions. However, the manuscript currently does not directly test the representation claim against the dominant HumanML3D local-relative representation under a matched recipe, so the significance of the headline attribution remains conditional on an additional controlled experiment.

major comments (4)
  1. [§4.2, Table 1] The central claim that absolute coordinates outperform the HumanML3D local-relative representation is not directly tested. The controlled rows in Table 1 compare 'Absolute+Redundancy' (an InterGen-style absolute representation with redundant kinematic features) against plain absolute coordinates, both under concatenation conditioning. The local-relative, kinematic-aware HumanML3D encoding is never trained with the same ACMDM recipe, and the later rows that add AdaLN, latent AE, and velocity prediction are run only for the absolute representation. Because the abstract and introduction attribute the gains specifically to abandoning the HumanML3D local-relative formulation, this missing cell is load-bearing. Please add a matched experiment that trains the same ACMDM model (ideally with the full optimal recipe: AdaLN + Causal AE + velocity prediction) on the HumanML3D representation, or revise the claim to be explicitly about absolute coordinates versus absolute coordinates with redundancy.
  2. [§4.2 and Table 2] The state-of-the-art comparison confounds the representation change with changes in architecture, tokenization, latent autoencoder, conditioning mechanism, diffusion objective, and model scale. The comparison of ACMDM-XL-PS2 (FID 0.058, R-Precision Top-1 0.522) against MARDM-v (FID 0.114, R-Precision Top-1 0.500) therefore cannot establish that absolute coordinates -- rather than the overall recipe -- are responsible for the improvement. A matched comparison is needed: either apply the absolute-coordinate representation within the MARDM recipe, or train the ACMDM recipe on the HumanML3D local-relative representation. Without such an experiment, the claim that 'absolute coordinates make motion generation easy' is not causally supported.
  3. [§4.1, Evaluation Metrics] All metrics are computed under the re-evaluation framework of [66], which is the authors' own prior work, and the paper does not report results under the original HumanML3D evaluation protocol used in most published baselines. Since metric details (including the choice of motion features and how they are extracted) directly affect FID and R-Precision numbers, the lack of the original-protocol numbers makes cross-paper comparisons nonstandard and difficult for readers to relate to the existing literature. Please report results under the original HumanML3D protocol as well, or provide a clear calibration between the two protocols.
  4. [§4.4, Table 3] The controllable generation comparison also mixes multiple variables: the baseline methods use different architectures, different motion representations, and some rely on classifier guidance, while ACMDM adds a ControlNet branch to the main model. The conclusion that absolute coordinates 'naturally support' control without guidance is plausible, but the current evidence does not isolate the representation effect. Please include an ablation that uses the same ControlNet-based framework with a local-relative representation (or with the HumanML3D representation) to show that the representational change, rather than the ControlNet design, is responsible for the improved control accuracy and the elimination of classifier guidance.
minor comments (5)
  1. [Table 3 and Table A2] In the upper-body editing section, OmniControl is cited as [120] and MotionLCM V2+CtrlNet as [120], but OmniControl is [103] and MotionLCM V2 is [14]; please correct these reference labels.
  2. [§4.5, Table 4] The text states that all mesh-generation variants achieve results 'competitive with the best text-to-joint generation models,' but Table 4 contains no direct mesh-generation baselines and no joint-level baseline under the same evaluation features; please add a comparative baseline or qualify the claim.
  3. [Introduction, Contributions] The third contribution bullet contains a grammatical issue: 'promising generalizes beyond joints' should be 'promising generalization beyond joints' or 'promisingly generalizes beyond joints.'
  4. [§4.2, Table 1 caption] The table caption says 'The results indicate that kinematic-aware redundancy is not necessary,' but the table only compares absolute representations with and without redundancy; please rephrase to match the actual comparison, e.g., 'redundant kinematic features are not necessary for absolute-coordinate diffusion.'
  5. [Figure 3 caption] The caption text 'MARDMMotionLCM V2MDM' appears to be a formatting artifact from concatenated method names; please separate the method names clearly.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the representation claim rests on an in-paper controlled ablation and external HumanML3D evaluation; the self-citation to the authors' earlier framework and MARDM baseline is not load-bearing.

full rationale

The paper's contribution is empirical rather than derivational: no equation in Sections 3-4 defines the reported FID/R-Precision numbers in terms of a fitted input, and no prediction reduces by construction to a training target. The central claim that kinematic-aware redundancy is unnecessary is tested inside the paper: Table 1 fixes the architecture and conditioning and compares Absolute+Redundancy (InterGen-style) against plain Absolute coordinates, with the latter winning under velocity prediction. The broader SOTA comparison in Table 2 is computed on the external HumanML3D benchmark rather than being inherited from a citation. Reference [66] is a self-citation by the same group and supplies the evaluation harness and the MARDM baseline, but the paper re-runs those numbers and the main ablation is self-contained, so the self-citation is not load-bearing in the sense of making the result true by definition. The absence of an exact cell training HumanML3D's local-relative representation under the full ACMDM recipe (AdaLN + causal AE + v-prediction) is a real comparability limitation and belongs in correctness risk, not circularity; it does not show that ACMDM's absolute-coordinate result is equivalent to its input.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central claim rests on empirical comparisons rather than theoretical derivation. The main free choices are architectural hyperparameters (patch size, CFG, latent dimensions) that were selected via ablations. The key domain assumption is that absolute coordinates are sufficient and that the evaluation metrics are valid. No new entities are introduced.

free parameters (6)
  • patch_size = 1x2 (best, smaller is better)
    Chosen by hand; decreasing patch size improves FID and R-Precision across all model sizes.
  • cfg_scale = 3.0 (text-to-motion), 2.5 (trajectory), 4.5 (mesh)
    Hand-selected per task; influences quality and control adherence.
  • latent_channel = 4
    Motion autoencoder output channel count, chosen without reported sweep.
  • temporal_downsampling_factor = 4
    Motion autoencoder downsampling factor, chosen by design.
  • sampling_steps = not reported
    Number of inference steps affects AITS and quality; not specified in the paper.
  • control_l2_loss_weight = not reported
    Weight for the ControlNet L2 loss on control signals; not specified.
assumptions (5)
  • domain assumption Absolute joint coordinates in global space fully capture human motion for generation and control.
    The paper's central premise; they argue kinematic features are unnecessary, assuming joint positions encode sufficient information for fidelity and downstream tasks.
  • domain assumption HumanML3D evaluation metrics (FID, R-Precision, Matching) are valid proxies for motion fidelity and text alignment.
    All conclusions are drawn from these metrics, which are standard but imperfect measures.
  • domain assumption CLIP-B/32 text embeddings provide sufficient semantic conditioning for text-to-motion generation.
    The model relies entirely on a frozen CLIP text encoder; this is a common but unverified assumption in the ablation.
  • standard math The v-prediction (flow-matching) diffusion objective is a valid and stable training target.
    Adopted from prior work [57]; the paper empirically finds it superior but does not derive it.
  • domain assumption The HumanML3D dataset is representative, and results transfer to KIT and mesh tasks.
    The paper evaluates on HumanML3D and KIT, but only HumanML3D is used for the main claims.

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Cite this review

Pith. "Pith review of Absolute Coordinates Make Motion Generation Easy." pith.science (2026). https://pith.science/paper/NNHQWLG6

@misc{pith2026250519377,
  author       = {Pith},
  title        = {Pith review of: Absolute Coordinates Make Motion Generation Easy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NNHQWLG6}},
  note         = {Machine review of arXiv:2505.19377}
}
read the original abstract

State-of-the-art text-to-motion generation models rely on the kinematic-aware, local-relative motion representation popularized by HumanML3D, which encodes motion relative to the pelvis and to the previous frame with built-in redundancy. While this design simplifies training for earlier generation models, it introduces critical limitations for diffusion models and hinders applicability to downstream tasks. In this work, we revisit the motion representation and propose a radically simplified and long-abandoned alternative for text-to-motion generation: absolute joint coordinates in global space. Through systematic analysis of design choices, we show that this formulation achieves significantly higher motion fidelity, improved text alignment, and strong scalability, even with a simple Transformer backbone and no auxiliary kinematic-aware losses. Moreover, our formulation naturally supports downstream tasks such as text-driven motion control and temporal/spatial editing without additional task-specific reengineering and costly classifier guidance generation from control signals. Finally, we demonstrate promising generalization to directly generate SMPL-H mesh vertices in motion from text, laying a strong foundation for future research and motion-related applications.

Figures

Figures reproduced from arXiv: 2505.19377 by the authors.

Figure 1
Figure 1. Absolute coordinates make motion generation easy. Here we show that our model pro￾duces motion of higher fidelity, has better controllability, and reports promising results of generating SMPL-H meshes directly. Abstract State-of-the-art text-to-motion generation models rely on the kinematic-aware, local-relative motion representation popularized by HumanML3D, which encodes motion relative to the pelvis and to the pr… view at source ↗
Figure 2
Figure 2. Overview of our proposed ACMDM. (a) Left: The raw/latent absolute coordinates representation is patchified and processed through a sequence of ACMDM blocks. Right: Details of ACMDM blocks, where we experiment with two conditioning variants: concatenation and AdaLN. (b) ControlNet-augmented ACMDM for controllable motion generation: Structured control signals are separately encoded and fused into the ACMDM generation … view at source ↗
Figure 3
Figure 3. Visual comparisons of generated motion between ACMDM and state-of-the-art methods. ACMDM generates more realistic motion that accurately follows the textual condition. within generated motions), Foot Skating Ratio, Trajectory Error, Location Error, and Average Joint Error (accuracy of controlled joints at keyframes). Metrics are averaged over five levels of control intensity (1%, 2%, 5%, 25%, 100%). During training,… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Scaling of ACMDM with model ca￾pacity and decreasing patch size. We use red for S, orange for B, green for L, and blue for XL, with color gradients indicating decreasing patch sizes. ACMDM exhibits strong scalability, with performance consistently improving as model si…

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Reference graph

Works this paper leans on

131 extracted references · 22 canonical work pages · cited by 6 Pith papers

  1. [66]

    Rethinking diffusion for text-driven human motion generation.arXiv preprint arXiv:2411.16575, 2024

    Zichong Meng, Yiming Xie, Xiaogang Peng, Zeyu Han, and Huaizu Jiang. Rethinking diffusion for text-driven human motion generation.arXiv preprint arXiv:2411.16575, 2024

    arXiv 2024

  2. [1]

    Language2pose: Natural language grounded pose forecasting

    Chaitanya Ahuja and Louis-Philippe Morency. Language2pose: Natural language grounded pose forecasting. In2019 International conference on 3D vision (3DV), pages 719–728. IEEE, 2019

    2019

  3. [2]

    LEAD: Latent Realignment for Human Motion Diffusion

    Nefeli Andreou, Xi Wang, Victoria Fernández Abrevaya, Marie-Paule Cani, Yiorgos Chrysan- thou, and Vicky Kalogeiton. Lead: Latent realignment for human motion diffusion.arXiv preprint arXiv:2410.14508, 2024

    work page Pith review arXiv 2024

  4. [3]

    Make-an- animation: Large-scale text-conditional 3d human motion generation

    Samaneh Azadi, Akbar Shah, Thomas Hayes, Devi Parikh, and Sonal Gupta. Make-an- animation: Large-scale text-conditional 3d human motion generation. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 15039–15048, 2023

    2023

  5. [4]

    Autoencoders.Machine learning for data science handbook: data mining and knowledge discovery handbook, pages 353–374, 2023

    Dor Bank, Noam Koenigstein, and Raja Giryes. Autoencoders.Machine learning for data science handbook: data mining and knowledge discovery handbook, pages 353–374, 2023

    2023

  6. [5]

    Text2gestures: A transformer-based network for generating emotive body gestures for virtual agents

    Uttaran Bhattacharya, Nicholas Rewkowski, Abhishek Banerjee, Pooja Guhan, Aniket Bera, and Dinesh Manocha. Text2gestures: A transformer-based network for generating emotive body gestures for virtual agents. In2021 IEEE virtual reality and 3D user interfaces (VR), pages 1–10. IEEE, 2021

  7. [6]

    Keep it smpl: Automatic estimation of 3d human pose and shape from a single image

    Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, and Michael J Black. Keep it smpl: Automatic estimation of 3d human pose and shape from a single image. InComputer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V 14, pages 561–578. Springer, 2016

    2016

  8. [7]

    Ready-to-react: Online reaction policy for two-character interaction generation

    Zhi Cen, Huaijin Pi, Sida Peng, Qing Shuai, Yujun Shen, Hujun Bao, Xiaowei Zhou, and Ruizhen Hu. Ready-to-react: Online reaction policy for two-character interaction generation. InThe Thirteenth International Conference on Learning Representations, 2025

    2025

Show all 131 references
  1. [8]

    Maskgit: Masked generative image transformer

    Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative image transformer. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11315–11325, 2022

    2022

  2. [9]

    Sitcom-crafter: A plot-driven human motion generation system in 3d scenes

    Jianqi Chen, Panwen Hu, Xiaojun Chang, Zhenwei Shi, Michael Christian Kampffmeyer, and Xiaodan Liang. Sitcom-crafter: A plot-driven human motion generation system in 3d scenes. arXiv preprint arXiv:2410.10790, 2024

    2024 arXiv

  3. [10]

    Pay attention and move better: Harnessing attention for interactive motion generation and training-free editing.arXiv preprint arXiv:2410.18977, 2024

    Ling-Hao Chen, Shunlin Lu, Wenxun Dai, Zhiyang Dou, Xuan Ju, Jingbo Wang, Taku Komura, and Lei Zhang. Pay attention and move better: Harnessing attention for interactive motion generation and training-free editing.arXiv preprint arXiv:2410.18977, 2024

    arXiv

  4. [11]

    Taming diffusion probabilistic models for character control

    Rui Chen, Mingyi Shi, Shaoli Huang, Ping Tan, Taku Komura, and Xuelin Chen. Taming diffusion probabilistic models for character control. InACM SIGGRAPH 2024 Conference Papers, pages 1–10, 2024

    2024

  5. [12]

    Executing your commands via motion diffusion in latent space

    Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao Chen, and Gang Yu. Executing your commands via motion diffusion in latent space. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18000–18010, 2023

    2023

  6. [13]

    Laserhuman: Language-guided scene-aware human motion generation in free environment.arXiv preprint arXiv:2403.13307, 2024

    Peishan Cong, Ziyi Wang, Zhiyang Dou, Yiming Ren, Wei Yin, Kai Cheng, Yujing Sun, Xiaoxiao Long, Xinge Zhu, and Yuexin Ma. Laserhuman: Language-guided scene-aware human motion generation in free environment.arXiv preprint arXiv:2403.13307, 2024

    2024 arXiv

  7. [14]

    Real-time controllable motion generation via latent consistency model.arXiv preprint, 2024

    Wenxun Dai, Ling-Hao Chen, Yufei Huo, Jingbo Wang, Jinpeng Liu, Bo Dai, and Yansong Tang. Real-time controllable motion generation via latent consistency model.arXiv preprint, 2024

    2024

  8. [15]

    Mo- tionlcm: Real-time controllable motion generation via latent consistency model

    Wenxun Dai, Ling-Hao Chen, Jingbo Wang, Jinpeng Liu, Bo Dai, and Yansong Tang. Mo- tionlcm: Real-time controllable motion generation via latent consistency model. InECCV, pages 390–408, 2025. 10

    2025

  9. [16]

    Diffusion models beat gans on image synthesis

    Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780–8794, 2021

    2021

  10. [17]

    Cg-hoi: Contact-guided 3d human-object interaction genera- tion

    Christian Diller and Angela Dai. Cg-hoi: Contact-guided 3d human-object interaction genera- tion. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19888–19901, 2024

    2024

  11. [18]

    An image is worth 16x16 words: Transformers for image recognition at scale.arXiv preprint arXiv:2010.11929, 2020

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale.arXiv preprint arXiv:...

    2010 arXiv

  12. [19]

    Synthesis of compositional animations from textual descriptions

    Anindita Ghosh, Noshaba Cheema, Cennet Oguz, Christian Theobalt, and Philipp Slusallek. Synthesis of compositional animations from textual descriptions. InProceedings of the IEEE/CVF international conference on computer vision, pages 1396–1406, 2021

    2021

  13. [20]

    Remos: Reactive 3d motion synthesis for two-person interactions.arXiv preprint arXiv:2311.17057, 2023

    Anindita Ghosh, Rishabh Dabral, Vladislav Golyanik, Christian Theobalt, and Philipp Slusallek. Remos: Reactive 3d motion synthesis for two-person interactions.arXiv preprint arXiv:2311.17057, 2023

    2023 arXiv

  14. [21]

    Diffusion implicit policy for unpaired scene-aware motion synthesis.arXiv preprint arXiv:2412.02261, 2024

    Jingyu Gong, Chong Zhang, Fengqi Liu, Ke Fan, Qianyu Zhou, Xin Tan, Zhizhong Zhang, Yuan Xie, and Lizhuang Ma. Diffusion implicit policy for unpaired scene-aware motion synthesis.arXiv preprint arXiv:2412.02261, 2024

    2024

  15. [22]

    Generative adversarial nets.Advances in neural information processing systems, 27, 2014

    Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets.Advances in neural information processing systems, 27, 2014

    2014

  16. [23]

    Momask: Gen- erative masked modeling of 3d human motions

    Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng. Momask: Gen- erative masked modeling of 3d human motions. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1900–1910, 2024

    1900

  17. [24]

    Generative human motion stylization in latent space.arXiv preprint arXiv:2401.13505, 2024

    Chuan Guo, Yuxuan Mu, Xinxin Zuo, Peng Dai, Youliang Yan, Juwei Lu, and Li Cheng. Generative human motion stylization in latent space.arXiv preprint arXiv:2401.13505, 2024

    2024 arXiv

  18. [25]

    Generating diverse and natural 3d human motions from text

    Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, and Li Cheng. Generating diverse and natural 3d human motions from text. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5152–5161, June 2022

    2022

  19. [26]

    Tm2t: Stochastic and tokenized modeling for the reciprocal generation of 3d human motions and texts

    Chuan Guo, Xinxin Zuo, Sen Wang, and Li Cheng. Tm2t: Stochastic and tokenized modeling for the reciprocal generation of 3d human motions and texts. InEuropean Conference on Computer Vision, pages 580–597. Springer, 2022

    2022

  20. [27]

    Action2motion: Conditioned generation of 3d human motions

    Chuan Guo, Xinxin Zuo, Sen Wang, Shihao Zou, Qingyao Sun, Annan Deng, Minglun Gong, and Li Cheng. Action2motion: Conditioned generation of 3d human motions. InProceedings of the 28th ACM International Conference on Multimedia, pages 2021–2029, 2020

    2021

  21. [28]

    Query-key normal- ization for transformers.arXiv preprint arXiv:2010.04245, 2020

    Alex Henry, Prudhvi Raj Dachapally, Shubham Pawar, and Yuxuan Chen. Query-key normal- ization for transformers.arXiv preprint arXiv:2010.04245, 2020

    2010 arXiv

  22. [29]

    Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

    2020

  23. [30]

    Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598, 2022

    Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598, 2022

    2022 arXiv

  24. [31]

    Motion flow matching for human motion synthesis and editing.arXiv preprint arXiv:2312.08895, 2023

    Vincent Tao Hu, Wenzhe Yin, Pingchuan Ma, Yunlu Chen, Basura Fernando, Yuki M Asano, Efstratios Gavves, Pascal Mettes, Bjorn Ommer, and Cees GM Snoek. Motion flow matching for human motion synthesis and editing.arXiv preprint arXiv:2312.08895, 2023

    2023 arXiv

  25. [32]

    Diffusion-based generation, optimization, and planning in 3d scenes

    Siyuan Huang, Zan Wang, Puhao Li, Baoxiong Jia, Tengyu Liu, Yixin Zhu, Wei Liang, and Song-Chun Zhu. Diffusion-based generation, optimization, and planning in 3d scenes. In CVPR, 2023. 11

    2023

  26. [33]

    Stablemofusion: Towards robust and efficient diffusion-based motion generation framework

    Yiheng Huang, Hui Yang, Chuanchen Luo, Yuxi Wang, Shibiao Xu, Zhaoxiang Zhang, Man Zhang, and Junran Peng. Stablemofusion: Towards robust and efficient diffusion-based motion generation framework. InProceedings of the 32nd ACM International Conference on Multimedia, pages 224–...

    2024

  27. [34]

    Como: Controllable motion generation through language guided pose code editing

    Yiming Huang, Weilin Wan, Yue Yang, Chris Callison-Burch, Mark Yatskar, and Lingjie Liu. Como: Controllable motion generation through language guided pose code editing. In European Conference on Computer Vision, pages 180–196. Springer, 2025

    2025

  28. [35]

    Scenemi: Motion in-betweening for modeling human-scene interactions.arXiv preprint arXiv:2503.16289, 2025

    Inwoo Hwang, Bing Zhou, Young Min Kim, Jian Wang, and Chuan Guo. Scenemi: Motion in-betweening for modeling human-scene interactions.arXiv preprint arXiv:2503.16289, 2025

    2025 arXiv

  29. [36]

    Intermask: 3d human interaction generation via collaborative masked modelling.arXiv preprint arXiv:2410.10010, 2024

    Muhammad Gohar Javed, Chuan Guo, Li Cheng, and Xingyu Li. Intermask: 3d human interaction generation via collaborative masked modelling.arXiv preprint arXiv:2410.10010, 2024

    2024 arXiv

  30. [37]

    Motiongpt: Human motion as a foreign language.Advances in Neural Information Processing Systems, 36:20067–20079, 2023

    Biao Jiang, Xin Chen, Wen Liu, Jingyi Yu, Gang Yu, and Tao Chen. Motiongpt: Human motion as a foreign language.Advances in Neural Information Processing Systems, 36:20067–20079, 2023

    2023

  31. [38]

    Scaling up dynamic human-scene interaction modeling

    Nan Jiang, Zhiyuan Zhang, Hongjie Li, Xiaoxuan Ma, Zan Wang, Yixin Chen, Tengyu Liu, Yixin Zhu, and Siyuan Huang. Scaling up dynamic human-scene interaction modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1737–1747, 2024

    2024

  32. [39]

    Act as you wish: Fine-grained control of motion diffusion model with hierarchical semantic graphs.Advances in Neural Information Processing Systems, 36, 2024

    Peng Jin, Yang Wu, Yanbo Fan, Zhongqian Sun, Wei Yang, and Li Yuan. Act as you wish: Fine-grained control of motion diffusion model with hierarchical semantic graphs.Advances in Neural Information Processing Systems, 36, 2024

    2024

  33. [40]

    Mas: Multi-view ancestral sampling for 3d motion generation using 2d diffusion

    Roy Kapon, Guy Tevet, Daniel Cohen-Or, and Amit H Bermano. Mas: Multi-view ancestral sampling for 3d motion generation using 2d diffusion. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1965–1974, 2024

    1965

  34. [41]

    Optimizing diffusion noise can serve as universal motion priors

    Korrawe Karunratanakul, Konpat Preechakul, Emre Aksan, Thabo Beeler, Supasorn Suwa- janakorn, and Siyu Tang. Optimizing diffusion noise can serve as universal motion priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1334–1345, 2024

    2024

  35. [42]

    Guided motion diffusion for controllable human motion synthesis

    Korrawe Karunratanakul, Konpat Preechakul, Supasorn Suwajanakorn, and Siyu Tang. Guided motion diffusion for controllable human motion synthesis. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 2151–2162, 2023

    2023

  36. [43]

    Flame: Free-form language-based motion syn- thesis & editing

    Jihoon Kim, Jiseob Kim, and Sungjoon Choi. Flame: Free-form language-based motion syn- thesis & editing. InProceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 8255–8263, 2023

    2023

  37. [44]

    Auto-encoding variational bayes, 2013

    Diederik P Kingma, Max Welling, et al. Auto-encoding variational bayes, 2013

    2013

  38. [45]

    Nifty: Neural object interaction fields for guided human motion synthesis

    Nilesh Kulkarni, Davis Rempe, Kyle Genova, Abhijit Kundu, Justin Johnson, David Fouhey, and Leonidas Guibas. Nifty: Neural object interaction fields for guided human motion synthesis. InCVPR, 2024

    2024

  39. [46]

    Controllable human-object interaction synthesis

    Jiaman Li, Alexander Clegg, Roozbeh Mottaghi, Jiajun Wu, Xavier Puig, and C Karen Liu. Controllable human-object interaction synthesis. InEuropean Conference on Computer Vision, pages 54–72. Springer, 2025

    2025

  40. [47]

    Lifting motion to the 3d world via 2d diffusion.arXiv preprint arXiv:2411.18808, 2024

    Jiaman Li, C Karen Liu, and Jiajun Wu. Lifting motion to the 3d world via 2d diffusion.arXiv preprint arXiv:2411.18808, 2024

    2024 arXiv

  41. [48]

    Object motion guided human motion synthesis.ACM Transactions on Graphics (TOG), 42(6):1–11, 2023

    Jiaman Li, Jiajun Wu, and C Karen Liu. Object motion guided human motion synthesis.ACM Transactions on Graphics (TOG), 42(6):1–11, 2023. 12

    2023

  42. [49]

    Mulsmo: Multimodal stylized motion generation by bidirectional control flow

    Zhe Li, Yisheng He, Lei Zhong, Weichao Shen, Qi Zuo, Lingteng Qiu, Zilong Dong, Lau- rence Tianruo Yang, and Weihao Yuan. Mulsmo: Multimodal stylized motion generation by bidirectional control flow. InarXiv 2412.09901, 2024

    2024 arXiv

  43. [50]

    Zhe Li, Weihao Yuan, Yisheng He, Lingteng Qiu, Shenhao Zhu, Xiaodong Gu, Weichao Shen, Yuan Dong, Zilong Dong, and Laurence T. Yang. Lamp: Language-motion pretraining for motion generation, retrieval, and captioning. InarXiv 2410.07093, 2024

    arXiv

  44. [51]

    Omg: Towards open-vocabulary motion generation via mixture of controllers

    Han Liang, Jiacheng Bao, Ruichi Zhang, Sihan Ren, Yuecheng Xu, Sibei Yang, Xin Chen, Jingyi Yu, and Lan Xu. Omg: Towards open-vocabulary motion generation via mixture of controllers. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 482...

    2024

  45. [53]

    Intergen: Diffusion-based multi-human motion generation under complex interactions.International Journal of Computer Vision, pages 1–21, 2024

    Han Liang, Wenqian Zhang, Wenxuan Li, Jingyi Yu, and Lan Xu. Intergen: Diffusion-based multi-human motion generation under complex interactions.International Journal of Computer Vision, pages 1–21, 2024

    2024

  46. [54]

    Shape my moves: Text-driven shape-aware synthesis of human motions.arXiv preprint arXiv:2504.03639, 2025

    Ting-Hsuan Liao, Yi Zhou, Yu Shen, Chun-Hao Paul Huang, Saayan Mitra, Jia-Bin Huang, and Uttaran Bhattacharya. Shape my moves: Text-driven shape-aware synthesis of human motions.arXiv preprint arXiv:2504.03639, 2025

    2025 arXiv

  47. [55]

    Lin, Lemeng Wu, Rodolfo Corona, Kevin W

    Angela S. Lin, Lemeng Wu, Rodolfo Corona, Kevin W. H. Tai, Qi-Xing Huang, and Raymond J. Mooney. Generating animated videos of human activities from natural language descriptions. arXiv preprint, 2018

    2018

  48. [56]

    Motion-x: A large-scale 3d expressive whole-body human motion dataset.Advances in Neural Information Processing Systems, 2023

    Jing Lin, Ailing Zeng, Shunlin Lu, Yuanhao Cai, Ruimao Zhang, Haoqian Wang, and Lei Zhang. Motion-x: A large-scale 3d expressive whole-body human motion dataset.Advances in Neural Information Processing Systems, 2023

    2023

  49. [57]

    Flow straight and fast: Learning to generate and transfer data with rectified flow.arXiv preprint arXiv:2209.03003, 2022

    Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow.arXiv preprint arXiv:2209.03003, 2022

    2022 arXiv

  50. [58]

    Revisit human-scene interaction via space occupancy

    Xinpeng Liu, Haowen Hou, Yanchao Yang, Yong-Lu Li, and Cewu Lu. Revisit human-scene interaction via space occupancy. InEuropean Conference on Computer Vision, pages 1–19. Springer, 2025

    2025

  51. [59]

    Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. Smpl: a skinned multi-person linear model.ACM Trans. Graph., 34(6), October 2015

    2015

  52. [60]

    Zero-shot human-object interaction synthesis with multimodal priors.arXiv preprint arXiv:2503.20118, 2025

    Yuke Lou, Yiming Wang, Zhen Wu, Rui Zhao, Wenjia Wang, Mingyi Shi, and Taku Ko- mura. Zero-shot human-object interaction synthesis with multimodal priors.arXiv preprint arXiv:2503.20118, 2025

    2025 arXiv

  53. [61]

    Diversemo- tion: Towards diverse human motion generation via discrete diffusion.arXiv preprint arXiv:2309.01372, 2023

    Yunhong Lou, Linchao Zhu, Yaxiong Wang, Xiaohan Wang, and Yi Yang. Diversemo- tion: Towards diverse human motion generation via discrete diffusion.arXiv preprint arXiv:2309.01372, 2023

    2023 arXiv

  54. [63]

    Scamo: Exploring the scaling law in autoregressive motion generation model.arXiv preprint arXiv:2412.14559, 2024

    Shunlin Lu, Jingbo Wang, Zeyu Lu, Ling-Hao Chen, Wenxun Dai, Junting Dong, Zhiyang Dou, Bo Dai, and Ruimao Zhang. Scamo: Exploring the scaling law in autoregressive motion generation model.arXiv preprint arXiv:2412.14559, 2024

    2024 arXiv

  55. [64]

    Contact-aware human motion generation from textual descriptions.arXiv preprint arXiv:2403.15709, 2024

    Sihan Ma, Qiong Cao, Jing Zhang, and Dacheng Tao. Contact-aware human motion generation from textual descriptions.arXiv preprint arXiv:2403.15709, 2024

    2024 arXiv

  56. [65]

    Amass: Archive of motion capture as surface shapes

    Naureen Mahmood, Nima Ghorbani, Nikolaus F Troje, Gerard Pons-Moll, and Michael J Black. Amass: Archive of motion capture as surface shapes. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 5442–5451, 2019. 13

    2019

  57. [67]

    Carnegie mellon university - cmu graphics lab - motion capture library.Carnegie Mellon University - CMU Graphics Lab - motion capture library, 2017

    Carnegie Mellon University CMU Graphics Lab motion capture library. Carnegie mellon university - cmu graphics lab - motion capture library.Carnegie Mellon University - CMU Graphics Lab - motion capture library, 2017

    2017

  58. [68]

    Dinov2: Learning robust visual features without supervision.arXiv preprint arXiv:2304.07193, 2023

    Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision.arXiv preprint arXiv:2304.07193, 2023

    2023 arXiv

  59. [69]

    Scalable diffusion models with transformers

    William Peebles and Saining Xie. Scalable diffusion models with transformers. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 4195–4205, 2023

    2023

  60. [70]

    Hoi-diff: Text-driven synthesis of 3d human-object interactions using diffusion models.arXiv preprint arXiv:2312.06553, 2023

    Xiaogang Peng, Yiming Xie, Zizhao Wu, Varun Jampani, Deqing Sun, and Huaizu Jiang. Hoi-diff: Text-driven synthesis of 3d human-object interactions using diffusion models.arXiv preprint arXiv:2312.06553, 2023

    2023 arXiv

  61. [71]

    Temos: Generating diverse human motions from textual descriptions

    Mathis Petrovich, Michael J Black, and Gül Varol. Temos: Generating diverse human motions from textual descriptions. InECCV, 2022

    2022

  62. [72]

    Black, and Gül Varol

    Mathis Petrovich, Michael J. Black, and Gül Varol. TMR: Text-to-motion retrieval using contrastive 3D human motion synthesis. InICCV, 2023

    2023

  63. [73]

    Multi-track timeline control for text-driven 3d human motion generation

    Mathis Petrovich, Or Litany, Umar Iqbal, Michael J Black, Gul Varol, Xue Bin Peng, and Davis Rempe. Multi-track timeline control for text-driven 3d human motion generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1911–1921, 2024

    1911

  64. [74]

    Motion-2-to-3: Leveraging 2d motion data to boost 3d motion generation.arXiv preprint arXiv:2412.13111, 2024

    Huaijin Pi, Ruoxi Guo, Zehong Shen, Qing Shuai, Zechen Hu, Zhumei Wang, Yajiao Dong, Ruizhen Hu, Taku Komura, Sida Peng, et al. Motion-2-to-3: Leveraging 2d motion data to boost 3d motion generation.arXiv preprint arXiv:2412.13111, 2024

    2024 arXiv

  65. [75]

    Hierarchical generation of human-object interactions with diffusion probabilistic models

    Huaijin Pi, Sida Peng, Minghui Yang, Xiaowei Zhou, and Hujun Bao. Hierarchical generation of human-object interactions with diffusion probabilistic models. InICCV, 2023

    2023

  66. [76]

    Controlmm: Controllable masked motion generation.arXiv preprint arXiv:2410.10780, 2024

    Ekkasit Pinyoanuntapong, Muhammad Usama Saleem, Korrawe Karunratanakul, Pu Wang, Hongfei Xue, Chen Chen, Chuan Guo, Junli Cao, Jian Ren, and Sergey Tulyakov. Controlmm: Controllable masked motion generation.arXiv preprint arXiv:2410.10780, 2024

  67. [77]

    Bamm: Bidirectional autoregressive motion model.arXiv preprint arXiv:2403.19435, 2024

    Ekkasit Pinyoanuntapong, Muhammad Usama Saleem, Pu Wang, Minwoo Lee, Srijan Das, and Chen Chen. Bamm: Bidirectional autoregressive motion model.arXiv preprint arXiv:2403.19435, 2024

    2024 arXiv

  68. [78]

    Mmm: Generative masked motion model

    Ekkasit Pinyoanuntapong, Pu Wang, Minwoo Lee, and Chen Chen. Mmm: Generative masked motion model. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1546–1555, 2024

    2024

  69. [79]

    The kit motion-language dataset

    Matthias Plappert, Christian Mandery, and Tamim Asfour. The kit motion-language dataset. Big data, 4(4):236–252, 2016

    2016

  70. [80]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. InInternational conference on machine learning, pag...

    2021

  71. [81]

    Trace and pace: Controllable pedestrian animation via guided trajectory diffusion

    Davis Rempe, Zhengyi Luo, Xue Bin Peng, Ye Yuan, Kris Kitani, Karsten Kreis, Sanja Fidler, and Or Litany. Trace and pace: Controllable pedestrian animation via guided trajectory diffusion. InCVPR, 2023

    2023

  72. [82]

    Mixermdm: Learnable composition of human motion diffusion models.arXiv preprint arXiv:2504.01019, 2025

    Pablo Ruiz-Ponce, German Barquero, Cristina Palmero, Sergio Escalera, and José García- Rodríguez. Mixermdm: Learnable composition of human motion diffusion models.arXiv preprint arXiv:2504.01019, 2025. 14

    2025 arXiv

  73. [83]

    Rumelhart, Geoffrey E

    David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. Learning internal represen- tations by error propagation. InSematic Scholar, 1986

    1986

  74. [84]

    Human motion diffusion as a generative prior.arXiv preprint arXiv:2303.01418, 2023

    Yonatan Shafir, Guy Tevet, Roy Kapon, and Amit H Bermano. Human motion diffusion as a generative prior.arXiv preprint arXiv:2303.01418, 2023

    2023 arXiv

  75. [85]

    Generating fine- grained human motions using chatgpt-refined descriptions.arXiv preprint arXiv:2312.02772, 2023

    Xu Shi, Chuanchen Luo, Junran Peng, Hongwen Zhang, and Yunlian Sun. Generating fine- grained human motions using chatgpt-refined descriptions.arXiv preprint arXiv:2312.02772, 2023

    2023 arXiv

  76. [86]

    Interactive character control with auto-regressive motion diffusion models.ACM Transactions on Graphics (TOG), 43(4):1–14, 2024

    Yi Shi, Jingbo Wang, Xuekun Jiang, Bingkun Lin, Bo Dai, and Xue Bin Peng. Interactive character control with auto-regressive motion diffusion models.ACM Transactions on Graphics (TOG), 43(4):1–14, 2024

    2024

  77. [87]

    Denoising diffusion implicit models.arXiv preprint arXiv:2010.02502, 2020

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models.arXiv preprint arXiv:2010.02502, 2020

    2010 arXiv

  78. [88]

    Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

    Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

    2024

  79. [89]

    Coma: Compositional human motion generation with multi- modal agents.arXiv preprint arXiv:2412.07320, 2024

    Shanlin Sun, Gabriel De Araujo, Jiaqi Xu, Shenghan Zhou, Hanwen Zhang, Ziheng Huang, Chenyu You, and Xiaohui Xie. Coma: Compositional human motion generation with multi- modal agents.arXiv preprint arXiv:2412.07320, 2024

    2024 arXiv

  80. [90]

    Motionclip: Exposing human motion generation to clip space

    Guy Tevet, Brian Gordon, Amir Hertz, Amit H Bermano, and Daniel Cohen-Or. Motionclip: Exposing human motion generation to clip space. InEuropean Conference on Computer Vision, pages 358–374. Springer, 2022

    2022

  81. [91]

    Closd: Closing the loop between simulation and diffusion for multi-task character control.arXiv preprint arXiv:2410.03441, 2024

    Guy Tevet, Sigal Raab, Setareh Cohan, Daniele Reda, Zhengyi Luo, Xue Bin Peng, Amit H Bermano, and Michiel van de Panne. Closd: Closing the loop between simulation and diffusion for multi-task character control.arXiv preprint arXiv:2410.03441, 2024

    arXiv

  82. [92]

    Human motion diffusion model

    Guy Tevet, Sigal Raab, Brian Gordon, Yoni Shafir, Daniel Cohen-or, and Amit Haim Bermano. Human motion diffusion model. InThe Eleventh International Conference on Learning Representations, 2023

    2023

  83. [93]

    Humos: Human motion model conditioned on body shape

    Shashank Tripathi, Omid Taheri, Christoph Lassner, Michael Black, Daniel Holden, and Carsten Stoll. Humos: Human motion model conditioned on body shape. InEuropean Conference on Computer Vision, pages 133–152. Springer, 2025

    2025

  84. [94]

    Attention is all you need.Advances in neural information processing systems, 30, 2017

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need.Advances in neural information processing systems, 30, 2017

    2017

  85. [95]

    Tlcontrol: Trajectory and language control for human motion synthesis.arXiv preprint arXiv:2311.17135, 2023

    Weilin Wan, Zhiyang Dou, Taku Komura, Wenping Wang, Dinesh Jayaraman, and Lingjie Liu. Tlcontrol: Trajectory and language control for human motion synthesis.arXiv preprint arXiv:2311.17135, 2023

    2023 arXiv

  86. [96]

    Diffusionphase: Motion diffusion in frequency domain.arXiv preprint arXiv:2312.04036, 2023

    Weilin Wan, Yiming Huang, Shutong Wu, Taku Komura, Wenping Wang, Dinesh Jayaraman, and Lingjie Liu. Diffusionphase: Motion diffusion in frequency domain.arXiv preprint arXiv:2312.04036, 2023

    2023 arXiv

  87. [97]

    Towards diverse and natural scene-aware 3d human motion synthesis

    Jingbo Wang, Yu Rong, Jingyuan Liu, Sijie Yan, Dahua Lin, and Bo Dai. Towards diverse and natural scene-aware 3d human motion synthesis. In2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), page 20428–20437. IEEE, June 2022

    2022

  88. [98]

    Sims: Simulating human-scene interactions with real world script planning.arXiv preprint arXiv:2411.19921, 2024

    Wenjia Wang, Liang Pan, Zhiyang Dou, Zhouyingcheng Liao, Yuke Lou, Lei Yang, Jingbo Wang, and Taku Komura. Sims: Simulating human-scene interactions with real world script planning.arXiv preprint arXiv:2411.19921, 2024

    2024 arXiv

  89. [99]

    Text-controlled motion mamba: Text-instructed temporal grounding of human motion.arXiv preprint arXiv:2404.11375, 2024

    Xinghan Wang, Zixi Kang, and Yadong Mu. Text-controlled motion mamba: Text-instructed temporal grounding of human motion.arXiv preprint arXiv:2404.11375, 2024

    2024

  90. [100]

    Intercontrol: Generate human motion interactions by controlling every joint.arXiv preprint arXiv:2311.15864, 2023

    Zhenzhi Wang, Jingbo Wang, Dahua Lin, and Bo Dai. Intercontrol: Generate human motion interactions by controlling every joint.arXiv preprint arXiv:2311.15864, 2023. 15

    2023 arXiv

  91. [101]

    Thor: Text to human-object interaction diffusion via relation intervention.arXiv preprint arXiv:2403.11208, 2024

    Qianyang Wu, Ye Shi, Xiaoshui Huang, Jingyi Yu, Lan Xu, and Jingya Wang. Thor: Text to human-object interaction diffusion via relation intervention.arXiv preprint arXiv:2403.11208, 2024

    2024 arXiv

  92. [102]

    Motionstreamer: Streaming motion generation via diffusion-based autoregressive model in causal latent space.arXiv preprint arXiv:2503.15451, 2025

    Lixing Xiao, Shunlin Lu, Huaijin Pi, Ke Fan, Liang Pan, Yueer Zhou, Ziyong Feng, Xiaowei Zhou, Sida Peng, and Jingbo Wang. Motionstreamer: Streaming motion generation via diffusion-based autoregressive model in causal latent space.arXiv preprint arXiv:2503.15451, 2025

    2025 arXiv

  93. [103]

    Omnicontrol: Control any joint at any time for human motion generation

    Yiming Xie, Varun Jampani, Lei Zhong, Deqing Sun, and Huaizu Jiang. Omnicontrol: Control any joint at any time for human motion generation. InThe Twelfth International Conference on Learning Representations, 2024

    2024

  94. [104]

    Motionbank: A large-scale video motion benchmark with disentan- gled rule-based annotations.arXiv preprint arXiv:2410.13790, 2024

    Liang Xu, Shaoyang Hua, Zili Lin, Yifan Liu, Feipeng Ma, Yichao Yan, Xin Jin, Xiaokang Yang, and Wenjun Zeng. Motionbank: A large-scale video motion benchmark with disentan- gled rule-based annotations.arXiv preprint arXiv:2410.13790, 2024

    arXiv

  95. [105]

    Inter-x: Towards versatile human-human interac- tion analysis

    Liang Xu, Xintao Lv, Yichao Yan, Xin Jin, Shuwen Wu, Congsheng Xu, Yifan Liu, Yizhou Zhou, Fengyun Rao, Xingdong Sheng, et al. Inter-x: Towards versatile human-human interac- tion analysis. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pa...

    2024

  96. [106]

    Interdiff: Generating 3d human-object interactions with physics-informed diffusion

    Sirui Xu, Zhengyuan Li, Yu-Xiong Wang, and Liang-Yan Gui. Interdiff: Generating 3d human-object interactions with physics-informed diffusion. InICCV, 2023

    2023

  97. [107]

    Intermimic: Towards uni- versal whole-body control for physics-based human-object interactions.arXiv preprint arXiv:2502.20390, 2025

    Sirui Xu, Hung Yu Ling, Yu-Xiong Wang, and Liang-Yan Gui. Intermimic: Towards uni- versal whole-body control for physics-based human-object interactions.arXiv preprint arXiv:2502.20390, 2025

    2025

  98. [108]

    Interdreamer: Zero-shot text to 3d dynamic human-object interaction.arXiv preprint arXiv:2403.19652, 2024

    Sirui Xu, Ziyin Wang, Yu-Xiong Wang, and Liang-Yan Gui. Interdreamer: Zero-shot text to 3d dynamic human-object interaction.arXiv preprint arXiv:2403.19652, 2024

    2024

  99. [109]

    Guiding human- object interactions with rich geometry and relations.arXiv preprint arXiv:2503.20172, 2025

    Mengqing Xue, Yifei Liu, Ling Guo, Shaoli Huang, and Changxing Ding. Guiding human- object interactions with rich geometry and relations.arXiv preprint arXiv:2503.20172, 2025

    2025 arXiv

  100. [110]

    Paired recurrent autoencoders for bidirectional translation between robot actions and linguistic descriptions.IEEE Robotics and Automation Letters, 3(4):3441–3448, 2018

    Tatsuro Yamada, Hiroyuki Matsunaga, and Tetsuya Ogata. Paired recurrent autoencoders for bidirectional translation between robot actions and linguistic descriptions.IEEE Robotics and Automation Letters, 3(4):3441–3448, 2018

    2018

  101. [111]

    Cross-modal retrieval for motion and text via droptriple loss

    Sheng Yan, Yang Liu, Haoqiang Wang, Xin Du, Mengyuan Liu, and Hong Liu. Cross-modal retrieval for motion and text via droptriple loss. InProceedings of the 5th ACM International Conference on Multimedia in Asia, pages 1–7, 2023

    2023

  102. [112]

    Motionscript: Natural language descriptions for expressive 3d human motions.arXiv preprint arXiv:2312.12634, 2023

    Payam Jome Yazdian, Eric Liu, Rachel Lagasse, Hamid Mohammadi, Li Cheng, and Angelica Lim. Motionscript: Natural language descriptions for expressive 3d human motions.arXiv preprint arXiv:2312.12634, 2023

    2023

  103. [113]

    Generating human interaction motions in scenes with text control

    Hongwei Yi, Justus Thies, Michael J Black, Xue Bin Peng, and Davis Rempe. Generating human interaction motions in scenes with text control. InEuropean Conference on Computer Vision, pages 246–263. Springer, 2025

    2025

  104. [114]

    Socialgen: Modeling multi-human social interaction with language models

    Heng Yu, Juze Zhang, Changan Chen, Tiange Xiang, Yusu Fang, Juan Carlos Niebles, and Ehsan Adeli. Socialgen: Modeling multi-human social interaction with language models. arXiv preprint arXiv:2503.22906, 2025

    2025 arXiv

  105. [115]

    Mogents: Motion generation based on spatial-temporal joint modeling

    Weihao Yuan, Weichao Shen, Yisheng He, Yuan Dong, Xiaodong Gu, Zilong Dong, Liefeng Bo, and Qixing Huang. Mogents: Motion generation based on spatial-temporal joint modeling. arXiv preprint arXiv:2409.17686, 2024

    2024 arXiv

  106. [116]

    Physdiff: Physics-guided human motion diffusion model

    Ye Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat, and Jan Kautz. Physdiff: Physics-guided human motion diffusion model. InICCV, 2023

    2023

  107. [117]

    Energymogen: Compositional human motion generation with energy-based diffusion model in latent space.arXiv preprint arXiv:2412.14706, 2024

    Jianrong Zhang, Hehe Fan, and Yi Yang. Energymogen: Compositional human motion generation with energy-based diffusion model in latent space.arXiv preprint arXiv:2412.14706, 2024. 16

    2024 arXiv

  108. [118]

    T2m-gpt: Generating human motion from textual descriptions with discrete representations.arXiv preprint arXiv:2301.06052, 2023

    Jianrong Zhang, Yangsong Zhang, Xiaodong Cun, Shaoli Huang, Yong Zhang, Hongwei Zhao, Hongtao Lu, and Xi Shen. T2m-gpt: Generating human motion from textual descriptions with discrete representations.arXiv preprint arXiv:2301.06052, 2023

    2023 arXiv

  109. [119]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. InProceedings of the IEEE/CVF international conference on computer vision, pages 3836–3847, 2023

    2023

  110. [120]

    Motiondiffuse: Text-driven human motion generation with diffusion model.arXiv preprint arXiv:2208.15001, 2022

    Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu. Motiondiffuse: Text-driven human motion generation with diffusion model.arXiv preprint arXiv:2208.15001, 2022

    2022 arXiv

  111. [121]

    Remodiffuse: Retrieval-augmented motion diffusion model

    Mingyuan Zhang, Xinying Guo, Liang Pan, Zhongang Cai, Fangzhou Hong, Huirong Li, Lei Yang, and Ziwei Liu. Remodiffuse: Retrieval-augmented motion diffusion model. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 364–373, 2023

    2023

  112. [122]

    Motiongpt: Finetuned llms are general-purpose motion generators

    Yaqi Zhang, Di Huang, Bin Liu, Shixiang Tang, Yan Lu, Lu Chen, Lei Bai, Qi Chu, Nenghai Yu, and Wanli Ouyang. Motiongpt: Finetuned llms are general-purpose motion generators. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024

    2024

  113. [123]

    Motion mamba: Efficient and long sequence motion generation

    Zeyu Zhang, Akide Liu, Ian Reid, Richard Hartley, Bohan Zhuang, and Hao Tang. Motion mamba: Efficient and long sequence motion generation. InEuropean Conference on Computer Vision, pages 265–282. Springer, 2024

    2024

  114. [124]

    Motion anything: Any to motion generation.arXiv preprint arXiv:2503.06955, 2025

    Zeyu Zhang, Yiran Wang, Wei Mao, Danning Li, Rui Zhao, Biao Wu, Zirui Song, Bohan Zhuang, Ian Reid, and Richard Hartley. Motion anything: Any to motion generation.arXiv preprint arXiv:2503.06955, 2025

    2025 arXiv

  115. [125]

    Tedi: Temporally-entangled diffusion for long-term motion synthesis

    Zihan Zhang, Richard Liu, Rana Hanocka, and Kfir Aberman. Tedi: Temporally-entangled diffusion for long-term motion synthesis. InACM SIGGRAPH 2024 Conference Papers, pages 1–11, 2024

    2024

  116. [126]

    Dart: A diffusion-based autoregressive motion model for real-time text-driven motion control.arXiv preprint arXiv:2410.05260, 2024

    Kaifeng Zhao, Gen Li, and Siyu Tang. Dart: A diffusion-based autoregressive motion model for real-time text-driven motion control.arXiv preprint arXiv:2410.05260, 2024

    2024 arXiv

  117. [127]

    Synthesizing diverse human motions in 3d indoor scenes

    Kaifeng Zhao, Yan Zhang, Shaofei Wang, Thabo Beeler, and Siyu Tang. Synthesizing diverse human motions in 3d indoor scenes. InInternational conference on computer vision (ICCV), 2023

    2023

  118. [128]

    Attt2m: Text-driven human motion generation with multi-perspective attention mechanism

    Chongyang Zhong, Lei Hu, Zihao Zhang, and Shihong Xia. Attt2m: Text-driven human motion generation with multi-perspective attention mechanism. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 509–519, 2023

    2023

  119. [129]

    Smoodi: Stylized motion diffusion model

    Lei Zhong, Yiming Xie, Varun Jampani, Deqing Sun, and Huaizu Jiang. Smoodi: Stylized motion diffusion model. InEuropean Conference on Computer Vision, pages 405–421. Springer, 2025

    2025

  120. [130]

    Emdm: Efficient motion diffusion model for fast and high-quality motion generation

    Wenyang Zhou, Zhiyang Dou, Zeyu Cao, Zhouyingcheng Liao, Jingbo Wang, Wenjia Wang, Yuan Liu, Taku Komura, Wenping Wang, and Lingjie Liu. Emdm: Efficient motion diffusion model for fast and high-quality motion generation. InEuropean Conference on Computer Vision, pages 18–38. S...

    2025

  121. [131]

    Fully convolutional mesh autoencoder using efficient spatially varying kernels.Ad- vances in neural information processing systems, 33:9251–9262, 2020

    Yi Zhou, Chenglei Wu, Zimo Li, Chen Cao, Yuting Ye, Jason Saragih, Hao Li, and Yaser Sheikh. Fully convolutional mesh autoencoder using efficient spatially varying kernels.Ad- vances in neural information processing systems, 33:9251–9262, 2020

    2020

  122. [132]

    Infinidreamer: Arbitrarily long human motion generation via segment score distillation.arXiv preprint arXiv:2411.18303, 2024

    Wenjie Zhuo, Fan Ma, and Hehe Fan. Infinidreamer: Arbitrarily long human motion generation via segment score distillation.arXiv preprint arXiv:2411.18303, 2024

    2024

  123. [133]

    Parco: Part-coordinating text-to-motion synthesis

    Qiran Zou, Shangyuan Yuan, Shian Du, Yu Wang, Chang Liu, Yi Xu, Jie Chen, and Xiangyang Ji. Parco: Part-coordinating text-to-motion synthesis. InEuropean Conference on Computer Vision, pages 126–143. Springer, 2025. 17 Appendix We further discuss our proposed approach with the...

    2025

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.