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REVIEW 3 major objections 5 minor 1 cited by

RMD: A Simple Baseline for More General Human Motion Generation via Training-free Retrieval-Augmented Motion Diffuse

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Training-free RMD beats text-to-motion baselines on out-of-distribution data

desk verdict A simple, honest training-free retrieval baseline for text-to-motion, with gains that are real but smaller than claimed once you account for test-set t0 selection. read the letter →

arxiv 2412.04343 v1 pith:T2QNP22G submitted 2024-12-05 cs.CV cs.AIcs.GR

Zhouyingcheng Liao , Mingyuan Zhang , Wenjia Wang , Lei Yang , Taku Komura This is my paper · ORCID
classification cs.CVcs.AIcs.GR
keywords text-to-motiongenerationretrieval-augmentedmotiondiffusionout-of-distributiongeneralizationlargelanguagemodelSDEdithumansynthesis
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

This paper introduces RMD, a training-free retrieval-augmented pipeline for text-to-motion generation. The method claims that by decomposing a text prompt into body-part descriptions with an LLM, retrieving matching motions for each part from an external database, recomposing them, and then refining the result with a pre-trained MotionDiffuse model under SDEdit-style noise-and-denoise, it achieves state-of-the-art R-Precision and MM Dist on HumanML3D without any parameter updates. Its main advantage is on out-of-distribution data: on the Mixamo cross-domain test and in a user study with real-world prompts, RMD outperforms existing baselines. Because motion datasets are small and real-world prompts are far from training text distributions, a training-free plug-in that improves generalization could make text-to-motion systems more practical.

What carries the argument

The central machinery is a three-level hierarchical decompose-retrieve-recompose pipeline followed by SDEdit refinement. An LLM decomposition agent splits each prompt and each database motion description into half-body (upper/lower) and fine-grained (head, torso, left arm, right arm, lower body, trajectory) descriptions. A retrieval agent scores each query part against database entries using CLIP text embeddings with a length-aware similarity term (Eq. 1) and uses an LLM to pick among k=5 decomposition candidates. Retrieved parts are recombined into a full-body motion by concatenating joint quaternions, rescaled to the query length via SLERP and linear interpolation. The composed 'guided motion' is then refined by adding Gaussian noise up to time t0=0.96 and running the pretrained MotionDiffuse denoiser from t0 to 0, conditioned only on the original text prompt.

What would settle it

On the HumanML3D test set, replace the retrieved guided motion with a randomly selected motion of matching length from the retrieval database before the SDEdit refinement step. If R-Precision and MM Dist stay at the same level as the full RMD results instead of dropping toward the base MotionDiffuse numbers, then the diffusion refinement is ignoring the guided motion and the retrieval stage is not contributing. A second check: feed retrieval clips whose text contradicts the prompt (e.g., 'running' for a 'walking' prompt) and see whether the final output follows the prompt or the retrieved clip.

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Extended reading notes

Core claim

The central claim is that a pretrained motion diffusion model can serve as a sufficient prior to repair motions assembled from retrieved body-part clips, and that this two-stage pipeline, without any training, outperforms both trained retrieval-augmented models and recent generative baselines. On HumanML3D, RMD reports the best top-1/2/3 R-Precision and Multi-Modal Distance, with a slight drop in MultiModality relative to the base MotionDiffuse. On the cross-domain Mixamo test, it reports the best R-Precision scores, and the percentage of queries requiring fine-grained decomposition rises from 5.9% on HumanML3D to 36.9% on Mixamo and 51.5% on user-collected real-world prompts, supporting the paper's argument that current benchmarks under-represent compositional OOD motions.

Load-bearing premise

The refinement step assumes that at the chosen noise level t0=0.96, the pretrained diffusion model will remove the artifacts of compositional stitching while preserving the specific body-part semantics the retrieval stage added, even though the refinement is conditioned only on the original text prompt, not on the retrieved part descriptions.

Editorial extensions

If this is right

  • RMD improves MotionDiffuse on HumanML3D across all metrics except a small MultiModality drop, reaching the best R-Precision and MM Dist among compared methods.
  • On the Mixamo cross-domain benchmark, RMD achieves the best R-Precision, showing a clear advantage over in-domain-strong baselines when tested on a different motion distribution.
  • Real-world OOD prompts trigger far more fine-grained decomposition than HumanML3D test prompts (51.5% versus 5.9%), indicating a large gap between standard benchmarks and practical usage.
  • RMD gives consistent gains across several MotionDiffuse variants (startx/epsilon, 8-layer/12-layer), suggesting it is a robust plug-in rather than a special-case tuning.
  • When the retrieval database is larger than the diffusion model's training set, RMD's improvement grows and the optimal t0 drops from 0.96 to 0.8, showing retrieval becomes more valuable as the database expands.

Reading between the lines

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

  • This suggests that any diffusion-based text-to-motion model with a reverse-SDE formulation could be upgraded at inference time by the same decompose-retrieve-recompose-refine recipe, without retraining.
  • The LLM-driven decomposition could be automated further, for instance by learning to predict the decomposition level and t0 per prompt, which the paper flags as future work.
  • The fact that retrieval from the same training set improves performance implies current training algorithms do not fully exploit the training data, pointing toward memory-augmented inference as a cheaper alternative to scaling model size.
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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

3 major / 5 minor

Summary. The paper proposes RMD, a training-free retrieval-augmented pipeline for text-to-motion generation. Given a text prompt, an LLM-based decomposition agent decides whether to retrieve a full-body motion, half-body motions, or fine-grained body-part motions from an external database of motion-text pairs. The retrieved parts are composed into a guided motion, which is then refined by a pretrained MotionDiffuse model via SDEdit noise-and-denoise, conditioned on the original prompt. The authors evaluate RMD on HumanML3D, a cross-domain Mixamo test set, and a 33-prompt user study, reporting state-of-the-art R-Precision and MM Dist on HumanML3D and improved R-Precision on Mixamo. The central claim is that, without any training, RMD improves semantic alignment and generalization of text-to-motion models while reusing the base model unchanged.

Significance. If the claims hold, RMD is an appealingly simple and practical method: it requires no training, allows the retrieval database to be swapped, reuses body-part motions, and can improve an existing base model at inference time. The paper's explicit evaluation on a cross-domain Mixamo benchmark and on out-of-distribution user prompts is a useful contribution, and the authors are transparent about the base-model variants and ablations. The method's dependency on only frozen components (CLIP, GPT-4o, MotionDiffuse) makes it easy to build on. However, the empirical support for the central claims is currently weakened by test-set hyperparameter tuning, small and mixed gains, and a user study without statistical analysis.

major comments (3)
  1. [Section 4.4, Fig. 6] The main hyperparameters t0, tau_half, tau_fine, and lambda are selected on the HumanML3D test set. Figure 6 sweeps t0 on the test set and the authors state they "find the performance reaches a peak when t0=0.96 and choose this value for our main comparisons." No held-out validation split is described, and the same test set is then used to report Table 1. Because the reported improvement over the retrained MotionDiffuse baseline is small (+0.009 Top-1 R-Precision, +0.007 Top-2, +0.005 Top-3; FID 0.111 vs 0.141), it is possible that part or all of the gain is due to test-set tuning. The authors should either select t0 and thresholds on a held-out validation split and then report test results, or show a sensitivity analysis over a range of t0 values with confidence intervals and demonstrate that the conclusions are stable.
  2. [Section 3.3 and Supplementary Table 7] The causal role of the retrieval stage is not established. At t0=0.96 with 50 DDIM steps, the guided motion is corrupted with near-maximal noise, and the reverse process is largely driven by the text-conditioned diffusion prior rather than the retrieved composition. The paper acknowledges in Section 3.3 that t0 balances the guide and the prior, but it provides no quantitative evidence that the final output preserves the body-part semantics inserted by retrieval. The supplementary Table 7 shows that when the retrieval database and training set are mismatched, the optimal t0 shifts to about 0.8, which indicates the operating point is distribution-dependent and not a robust default. To support the claim that retrieval is load-bearing, the authors should include an analysis measuring the similarity between the final output and the guided motion, or an experiment that replaces the retrieved guided motion with a random or mean motion and evaluates the metrics at the same t0 values.
  3. [Section 4.3, Table 2 and Fig. 5] The cross-domain evidence for "notable advantages on out-of-distribution data" is weak and mixed. On the Mixamo benchmark, RMD improves R-Precision over MotionDiffuse (e.g., Top-1 10.015 vs 9.617) but its FID is worse (4.372 vs 4.318) and its Multimodality is worse (2.174 vs 2.333); the confidence intervals overlap on these metrics. The user study reports preference percentages from only 16 participants with no significance test, no error bars, and no inter-rater reliability. The paper should either present a formal significance analysis (e.g., bootstrap or paired test) or temper the claim from "significantly outperforms" to a more limited qualitative statement. Additionally, a cross-domain claim should be supported by more than one alternative dataset.
minor comments (5)
  1. [Section 3.1 vs. Supplementary Table 9] Section 3.1 defines fine-grained decomposition into "six body parts" (head, torso, left arm, right arm, lower body, trajectory), but the prompt in Supplementary Table 9 asks for seven lines: head, spine, left upper limb, right upper limb, left lower limb, right lower limb, and trajectory. Please align the number and naming of body parts between the method description and the LLM prompts.
  2. [Section 3.2, Eq. (1)] The sentence "taking the highest match score as the similarity score si" is ambiguous: it is unclear whether si is the score of the LLM-selected description or the maximum score across the k candidates. Please clarify the procedure.
  3. [Section 4.3, Table 4] The terms "startx" and "epsilon" are not defined in the main text; please define them when the table is first referenced (and note the typo "residule" in the caption).
  4. [Section 4.2] Please specify which version of MotionDiffuse is used as the base model in each table: the re-trained 12-layer variant or the original. The text says "we re-train MotionDiffuse," but the reader needs to know whether the comparison numbers for other methods were obtained under the same inference protocol (e.g., 50 DDIM steps).
  5. [Supplementary Table 6] Table 6 reports no confidence intervals for the SDS, DDS, and DNO baselines, unlike the other tables. Please report them for consistency.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: the retrieval-and-diffusion pipeline is training-free and uses independently published components, though the main t0 hyperparameter is selected on the evaluation test set, which is a mild self-confirmation concern rather than a logical circularity.

full rationale

RMD's claimed result is produced by an inference-time chain: LLM-based decomposition of the prompt, CLIP/LLM retrieval and recombination of motions from an external database, and SDEdit refinement with a pretrained MotionDiffuse prior. None of these stages defines the final output as its own input; the retrieval database is drawn from training data rather than from the test prompts, and the diffusion prior is an independently published model that is also varied across four MotionDiffuse variants in Table 4 and evaluated on KIT-ML in Table 8. The self-citations to MotionDiffuse [32] and ReMoDiffuse [33] are to public, reproducible systems, and the paper does not invoke any uniqueness theorem or use self-citation to forbid alternative designs; the SDEdit prior is grounded in external work [16] and PriorMDM [26]. The only load-bearing weakness is in Section 4.4 and Figure 6: t0 = 0.96 is chosen by sweeping the metric on the HumanML3D test set itself, and the same test set is then used for the headline Table 1 comparisons, so the in-domain SOTA gain is partly an in-sample selection artifact. Supplementary Table 7 further shows that the optimal t0 shifts to about 0.8 when the retrieval database and training set are mismatched, indicating that the fixed default is distribution-dependent. This is an evaluation-validity concern, not an equation-level circularity, because the output does not reduce to the fitted parameter by construction. Score 2 reflects minor self-citation and the test-set-tuned t0 rather than any genuine circular step.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

RMD introduces no new physical entities. The central claim rests on four domain assumptions about frozen text encoders, LLM semantic preservation, diffusion-prior refinement, and the validity of the evaluation protocol, plus one standard interpolation method. The main free parameters are inference hyperparameters, one of which (t0) was selected on the test set.

free parameters (4)
  • diffusion starting time t0 = 0.96 (0.9 for KIT-ML)
    Controls the trade-off between retrieval guidance and diffusion prior; selected as the peak of the HumanML3D test-set performance curve in Figure 6.
  • retrieval thresholds tau_half and tau_fine = 0.96 each
    Thresholds that decide whether full-body, half-body, or fine-grained retrieval is used; no separate validation procedure reported.
  • retrieval length coefficient lambda = 0.05
    Penalty weight on length mismatch in the retrieval score of Equation (1); hand-set.
  • LLM decomposition repetition count k = 5
    Number of LLM decompositions used per query; described as an empirical balance between accuracy and efficiency.
assumptions (5)
  • domain assumption CLIP ViT-B/32 text embeddings reliably rank semantic similarity between body-part-level motion descriptions.
    Equation (1) uses only CLIP text embeddings and a length penalty for retrieval; no motion-specific fine-tuning is applied.
  • domain assumption GPT-4o decomposition preserves the semantics of the original prompt and its LLM re-ranking selects the retrieved description that best matches the requested body part.
    Sections 3.1 and 3.2; if decomposition or re-ranking is unreliable, the composed motion will not match the prompt.
  • domain assumption The pretrained MotionDiffuse, used through SDEdit at t0=0.96, removes artifacts from cross-source composition while retaining the semantics introduced by retrieval.
    Section 3.3; the diffusion prior is conditioned only on the original text prompt, so any retrieved detail not in the prompt may be washed out.
  • standard math Quaternion SLERP and linear interpolation of translations from motions of different lengths produce a valid full-body skeleton after composition.
    Motion Composition paragraph in Section 3.2; interpolation math is standard, but its suitability for arbitrary cross-motion joint stitching is assumed.
  • domain assumption The nonstandard Mixamo evaluation protocol (retargeting to HumanML3D skeleton, splitting Mixamo to train an evaluator) yields fair cross-domain metrics.
    Section 4.3; no comparison with alternative evaluators or protocol sensitivity is provided.

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Cite this review

Pith. "Pith review of RMD: A Simple Baseline for More General Human Motion Generation via Training-free Retrieval-Augmented Motion Diffuse." pith.science (2026). https://pith.science/paper/T2QNP22G

@misc{pith2026241204343,
  author       = {Pith},
  title        = {Pith review of: RMD: A Simple Baseline for More General Human Motion Generation via Training-free Retrieval-Augmented Motion Diffuse},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T2QNP22G}},
  note         = {Machine review of arXiv:2412.04343}
}
read the original abstract

While motion generation has made substantial progress, its practical application remains constrained by dataset diversity and scale, limiting its ability to handle out-of-distribution scenarios. To address this, we propose a simple and effective baseline, RMD, which enhances the generalization of motion generation through retrieval-augmented techniques. Unlike previous retrieval-based methods, RMD requires no additional training and offers three key advantages: (1) the external retrieval database can be flexibly replaced; (2) body parts from the motion database can be reused, with an LLM facilitating splitting and recombination; and (3) a pre-trained motion diffusion model serves as a prior to improve the quality of motions obtained through retrieval and direct combination. Without any training, RMD achieves state-of-the-art performance, with notable advantages on out-of-distribution data.

Figures

Figures reproduced from arXiv: 2412.04343 by the authors.

Figure 1
Figure 1. Existing methods struggle with out-of-distribution motion generation due to two main challenges: (1) The compositional [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Method overview of RMD. Given a query text prompt, RMD uses a Decomposition Agent to split the prompt into body parts descriptions and a Retrieval Agent to search for corresponding motions. In the first stage, a hierarchical retrieval strategy is employed, prioritizing full-body to fine-grained motions. The process stops once the retrieval score meets the threshold, and the retrieved body parts are recomposed into a… view at source ↗
Figure 5
Figure 5. User study on OOD data. Our method outperforms others by a significant margin. Cross-dataset evaluation. HumanML3D has recently become the most popular text-to-motion benchmark, and re￾cent methods have achieved good quantitative performance on it. However, to better evaluate the generalization ability, we conduct a cross-dataset evaluation, where all the models are trained or retrieved using HumanML3D, and tested o… view at source ↗
Figures from the paper (3 more)
Figure 6
Figure 6. Figure 6: The performance on HumanML3D of different [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 3
Figure 3. Figure 3: Qualitative comparison between our method and previous methods. Our method achieves the best text alignment. A person is making a call emotionally. A person clutches their stomach and squats down in pain. A ballet dancer is spinning gracefully, her dance elegant and po…
Figure 4
Figure 4. Figure 4: Generated motions with various t0. t0 = 0 is the guided motion. t0 = 1 means starting from pure noise and is equivalent to MotionDiffuse. Since we use the same random seed for all samples here. t0 in between can be seen as an interpolation between the guided motion and…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation

    cs.CV 2025-12 conditional novelty 6.0 of 10

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Reference graph

Works this paper leans on

64 extracted references · 43 canonical work pages · cited by 1 Pith paper

  1. [1]

    Mixamo, https://www.mixamo.com, 2024. 2, 5

    work page 2024

  2. [2]

    A cross- dataset study for text-based 3d human motion retrieval

    L ´eore Bensabath, Mathis Petrovich, and Gul Varol. A cross- dataset study for text-based 3d human motion retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition , pages 1932–1940, 2024. 2, 3

    work page 1932

  3. [3]

    Retrieval-augmented diffusion models

    Andreas Blattmann, Robin Rombach, Kaan Oktay, Jonas M¨uller, and Bj ¨orn Ommer. Retrieval-augmented diffusion models. Advances in Neural Information Processing Sys- tems, 35:15309–15324, 2022. 2

    work page 2022

  4. [4]

    Digital life project: Autonomous 3d characters with social intelligence

    Zhongang Cai, Jianping Jiang, Zhongfei Qing, Xinying Guo, Mingyuan Zhang, Zhengyu Lin, Haiyi Mei, Chen Wei, Ruisi Wang, Wanqi Yin, et al. Digital life project: Autonomous 3d characters with social intelligence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 582–592, 2024. 2, 3

    work page 2024

  5. [5]

    Motionllm: Understanding human behaviors from human motions and videos

    Ling-Hao Chen, Shunlin Lu, Ailing Zeng, Hao Zhang, Benyou Wang, Ruimao Zhang, and Lei Zhang. Motionllm: Understanding human behaviors from human motions and videos. arXiv preprint arXiv:2405.20340, 2024. 2

    arXiv 2024

  6. [6]

    Re-imagen: Retrieval-augmented text-to-image gen- erator

    Wenhu Chen, Hexiang Hu, Chitwan Saharia, and William W Cohen. Re-imagen: Retrieval-augmented text-to-image gen- erator. arXiv preprint arXiv:2209.14491, 2022. 2

    arXiv 2022

  7. [7]

    Retrieval-augmented generation for large language models: A survey

    Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, 2023. 2

    arXiv 2023

  8. [8]

    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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5152–5161, 2022. 2, 5, 6, 1

    work page 2022

Show all 64 references
  1. [9]

    Momask: Generative masked model- ing of 3d human motions

    Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng. Momask: Generative masked model- ing of 3d human motions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 1900–1910, 2024. 1, 2, 6

    1900

  2. [10]

    Retrieval augmented language model pre- training

    Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. Retrieval augmented language model pre- training. In International conference on machine learning , pages 3929–3938. PMLR, 2020. 2

    2020

  3. [11]

    Animate-a-story: Storytelling with retrieval-augmented video generation

    Yingqing He, Menghan Xia, Haoxin Chen, Xiaodong Cun, Yuan Gong, Jinbo Xing, Yong Zhang, Xintao Wang, Chao Weng, Ying Shan, et al. Animate-a-story: Storytelling with retrieval-augmented video generation. arXiv preprint arXiv:2307.06940, 2023. 2

    2023 arXiv

  4. [12]

    Delta de- noising score

    Amir Hertz, Kfir Aberman, and Daniel Cohen-Or. Delta de- noising score. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2328–2337, 2023. 3, 1

    2023

  5. [13]

    Diffusion model-based image editing: A survey

    Yi Huang, Jiancheng Huang, Yifan Liu, Mingfu Yan, Jiaxi Lv, Jianzhuang Liu, Wei Xiong, He Zhang, Shifeng Chen, and Liangliang Cao. Diffusion model-based image editing: A survey. arXiv preprint arXiv:2402.17525, 2024. 3, 5

    2024 arXiv

  6. [14]

    Opti- mizing diffusion noise can serve as universal motion priors

    Korrawe Karunratanakul, Konpat Preechakul, Emre Aksan, Thabo Beeler, Supasorn Suwajanakorn, and Siyu Tang. Opti- mizing 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

  7. [15]

    Retrieval-augmented generation for knowledge-intensive nlp tasks

    Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K¨uttler, Mike Lewis, Wen-tau Yih, Tim Rockt ¨aschel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Syst...

    2020

  8. [16]

    Sdedit: Guided image synthesis and editing with stochastic differential equa- tions

    Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jia- jun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equa- tions. In ICLR, 2021. 2, 3, 5, 6

    2021

  9. [17]

    I2vedit: First-frame-guided video editing via image-to- video diffusion models

    Wenqi Ouyang, Yi Dong, Lei Yang, Jianlou Si, and Xingang Pan. I2vedit: First-frame-guided video editing via image-to- video diffusion models. arXiv preprint arXiv:2405.16537 ,

    arXiv

  10. [18]

    Temos: Generating diverse human motions from textual descriptions

    Mathis Petrovich, Michael J Black, and G ¨ul Varol. Temos: Generating diverse human motions from textual descriptions. In European Conference on Computer Vision , pages 480–

  11. [19]

    Tmr: Text-to-motion retrieval using contrastive 3d human motion synthesis

    Mathis Petrovich, Michael J Black, and G ¨ul Varol. Tmr: Text-to-motion retrieval using contrastive 3d human motion synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9488–9497, 2023. 2, 3

    2023

  12. [20]

    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. 2

    1911

  13. [21]

    The kit motion-language dataset

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

  14. [22]

    Dreamfusion: Text-to-3d using 2d diffusion

    Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Milden- hall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022. 3, 1

    2022 arXiv

  15. [23]

    Story-to-motion: Synthesizing infinite and control- lable character animation from long text

    Zhongfei Qing, Zhongang Cai, Zhitao Yang, and Lei Yang. Story-to-motion: Synthesizing infinite and control- lable character animation from long text. arXiv preprint arXiv:2311.07446, 2023. 1, 2

    2023 arXiv

  16. [24]

    Learn- ing transferable visual models from natural language super- vision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learn- ing transferable visual models from natural language super- vision. In International Conference on Machine Learning...

    2021

  17. [25]

    In- context retrieval-augmented language models

    Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Am- non Shashua, Kevin Leyton-Brown, and Yoav Shoham. In- context retrieval-augmented language models. Transactions 9 of the Association for Computational Linguistics , 11:1316– 1331, 2023. 2

    2023

  18. [26]

    Human motion diffusion as a generative prior

    Yonatan Shafir, Guy Tevet, Roy Kapon, and Amit H Bermano. Human motion diffusion as a generative prior. arXiv preprint arXiv:2303.01418, 2023. 3, 5

    2023 arXiv

  19. [27]

    Knn- diffusion: Image generation via large-scale retrieval

    Shelly Sheynin, Oron Ashual, Adam Polyak, Uriel Singer, Oran Gafni, Eliya Nachmani, and Yaniv Taigman. Knn- diffusion: Image generation via large-scale retrieval. arXiv preprint arXiv:2204.02849, 2022. 2

    2022 arXiv

  20. [28]

    Diffusion model-based video editing: A survey

    Wenhao Sun, Rong-Cheng Tu, Jingyi Liao, and Dacheng Tao. Diffusion model-based video editing: A survey. arXiv preprint arXiv:2407.07111, 2024. 3, 5

    2024 arXiv

  21. [29]

    Human motion diffu- sion model

    Guy Tevet, Sigal Raab, Brian Gordon, Yoni Shafir, Daniel Cohen-or, and Amit Haim Bermano. Human motion diffu- sion model. In The Eleventh International Conference on Learning Representations, 2023. 1, 2, 6

    2023

  22. [30]

    Tlcontrol: Trajectory and language control for human motion synthesis

    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. 1

    2023 arXiv

  23. [31]

    T2m-gpt: Generating human motion from textual descriptions with discrete representations

    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. 6

    2023 arXiv

  24. [32]

    Motiondif- fuse: Text-driven human motion generation with diffusion model

    Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu. Motiondif- fuse: Text-driven human motion generation with diffusion model. arXiv preprint arXiv:2208.15001, 2022. 1, 2, 5, 6

    2022 arXiv

  25. [33]

    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. arXiv preprint arXiv:2304.01116, 2023. 2, 3, 6

    2023 arXiv

  26. [34]

    Finemogen: Fine-grained spatio- temporal motion generation and editing

    Mingyuan Zhang, Huirong Li, Zhongang Cai, Jiawei Ren, Lei Yang, and Ziwei Liu. Finemogen: Fine-grained spatio- temporal motion generation and editing. Advances in Neural Information Processing Systems, 36:13981–13992, 2023. 1, 2, 6

    2023

  27. [35]

    Large motion model for unified multi-modal motion generation

    Mingyuan Zhang, Daisheng Jin, Chenyang Gu, Fangzhou Hong, Zhongang Cai, Jingfang Huang, Chongzhi Zhang, Xinying Guo, Lei Yang, Ying He, et al. Large motion model for unified multi-modal motion generation. arXiv preprint arXiv:2404.01284, 2024. 1, 2

    2024 arXiv

  28. [36]

    Mo- tiongpt: Finetuned llms are general-purpose motion genera- tors

    Yaqi Zhang, Di Huang, Bin Liu, Shixiang Tang, Yan Lu, Lu Chen, Lei Bai, Qi Chu, Nenghai Yu, and Wanli Ouyang. Mo- tiongpt: Finetuned llms are general-purpose motion genera- tors. arXiv preprint arXiv:2306.10900, 2023. 2

    2023 arXiv

  29. [37]

    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. In European Conference on Computer Vision, pages 18–38. ...

    2025

  30. [38]

    Typically, the retrieval database grows progressively as more motions are collected, while the diffusion model cannot always be updated in a timely manner

    Training and Retrieval Set Mismatch In real-world applications, the training set for the diffusion model and the retrieval database often do not align per- fectly. Typically, the retrieval database grows progressively as more motions are collected, while the diffusion model ca...

  31. [39]

    We employ Mo- tionDiffuse [32] as our baseline, which is trained using a 4- layer transformer architecture

    Evaluation on KIT-ML We further assess the efficacy of our approach on the KIT- ML [21] dataset, which comprises 3,911 motion sequences and 6,363 corresponding text descriptions. We employ Mo- tionDiffuse [32] as our baseline, which is trained using a 4- layer transformer arch...

  32. [40]

    The results are summarized in Table 6, where our method demonstrates a significant performance advan- tage over all other baselines

    Other Baseline Methods In addition to SDEdit, we explored several other methods that leverage diffusion priors to generate motions based on guided inputs. The results are summarized in Table 6, where our method demonstrates a significant performance advan- tage over all other ...

  33. [41]

    Table 11 shows examples of our retrieval agent

    LLM Agent Examples Table 10 shows examples of our motion decomposition agent. Table 11 shows examples of our retrieval agent. In constrast to the naive retrieval strategy, which directly chooses the retrieval entry with the highest similarity score, our method achieves better ...

  34. [42]

    Moves right hand towards a point

    Extends right arm forward. Moves right hand towards a point

  35. [43]

    Extends the right arm forward

  36. [44]

    Retrieved 1

    Right arm extends forward. Retrieved 1. Extends right arm forward to point or touch. descriptions 2. Extends right arm forward swiftly

  37. [45]

    Right arm extends forward and then lowers

  38. [46]

    Raises right arm quickly

  39. [47]

    Original This man bends down to pick up a box or ball, and then puts it on a shelf, which is on his chest level

    Right arm extended outward. Original This man bends down to pick up a box or ball, and then puts it on a shelf, which is on his chest level. Upper body 1. Arms extend forward to pick up an item and place it at waist height. decomposition 2. Bends at the waist, extends arms for...

  40. [48]

    Bending forward at the waist, arms reach down and then extend forward at waist level

  41. [49]

    Bends forward at the waist, reaches down, and raises arms to chest level

  42. [50]

    Retrieved 1

    Bends forward at the waist, reaches down and then places at chest level. Retrieved 1. Arms hang by the sides, then extend forward to pick up an item and return it. descriptions 2. Bends forward and grabs object with both hands, lifts it to chest level and rotates arms outwards

  43. [51]

    Bending forward at the waist while moving backward

  44. [52]

    Bends at the waist and raises arms in front

  45. [53]

    Original A man runs to the right then runs to the left then back to the middle

    Bends the upper body forward from the waist and then rises back up. Original A man runs to the right then runs to the left then back to the middle. Lower body 1. Legs push off the ground alternately in a rhythmic jog. decomposition 2. Legs engage in a steady, rhythmic stride s...

  46. [54]

    Legs alternate between stepping forward in a jogging pattern

  47. [55]

    Legs move in a rhythmic, alternating pattern to the left and right

  48. [56]

    Retrieved 1

    Legs alternate in a left to right jogging pattern, then return to the center. Retrieved 1. Legs push off the ground diagonally before transitioning into a run. descriptions 2. Legs move in a rhythmic stride, shifting left, right, and back to starting position

  49. [57]

    Legs moving in a repetitive jogging pattern, alternating between sides

  50. [58]

    Legs move in a rhythmic, alternating pattern

  51. [59]

    Original A person walks in a counterclockwise circle

    Legs move in a jogging pattern, changing direction from right to left, and back to the original spot. Original A person walks in a counterclockwise circle. Lower body 1. Feet move sequentially in a circular path, taking eight steps to complete the rotation. decomposition 2. Le...

  52. [60]

    Feet step sequentially in a circular path

  53. [61]

    Feet alternate in a steady rhythm, moving in a counterclockwise circular path

  54. [62]

    Retrieved 1

    Feet move in a continuous circular path. Retrieved 1. Feet step alternately to the right and then continue stepping to form a counterclockwise circular path. descriptions 2. Legs step in a circular path

  55. [63]

    Feet step alternately in a circular path

  56. [64]

    5.Feet move in a circular path in a clockwise direction

    Feet move in a counterclockwise circular path. 5.Feet move in a circular path in a clockwise direction. Table 11. Examples of retrieval agent. Bold face indicates the entry selected by the LLM retrieval agent. The box refers to the one selected by the naive retrieval strategy,...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.