Pith. sign in

REVIEW 4 major objections 5 minor 60 references

HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper presents HHI-Assist, a marker-based motion-capture dataset of people physically assisting each other in care tasks, and an interaction-aware diffusion model that predicts both partners' future poses more accurately than…

desk verdict New marker-based HHI assistance dataset is the real contribution; the IDD model is an incremental baseline and the 'significant/strong generalization' claims need statistical support. read the letter →

arxiv 2509.10096 v1 pith:SATHGWRD submitted 2025-09-12 cs.RO cs.CV

Saeed Saadatnejad , Reyhaneh Hosseininejad , Jose Barreiros , Katherine M. Tsui , Alexandre Alahi This is my paper · ORCID
classification cs.ROcs.CV
keywords humanmotionpredictionphysicalhuman-robotinteractionassistiveroboticscapturedatasetdenoisingdiffusionmodelinteraction-awareposeforecastingcaregivercarereceiverrobotlearning
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 is trying to establish that motion prediction for physical assistance—where one person helps another stand up, sit up, or move—should treat the two people as one coupled system, and that a generative model can learn that coupling from motion-capture data. To that end the authors collected HHI-Assist, 908 recordings of caregiver-care receiver pairs doing sit-to-stand, lay-to-sit, lay-to-stand, and unconstrained movements, with joint positions recorded at 120 Hz. They then built an interaction-aware denoising diffusion model (IDD) that predicts the future poses of both agents conditioned on both of their observed poses. On the benchmark the model reports lower prediction error than single-person baselines for both caregiver and care receiver, and it degrades gracefully on a transfer task it was not trained on. If the results hold, robots assisting people could react to a care receiver's motions earlier and more safely.

What carries the argument

The central object is a conditional denoising diffusion model: a Transformer with alternating temporal and spatial multi-head attention blocks, trained to predict the noise that was added to future pose sequences of both agents, conditioned on the concatenated observed pose sequences of both agents. At inference, the network iteratively denoises random Gaussian samples ($T=50$ steps) into pose futures that are consistent with the observed interaction. The supporting machinery is the HHI-Assist dataset itself, which supplies realistic, paired caregiver-care receiver motion for training and benchmarking.

What would settle it

Recompute MPJPE per test clip and run a paired significance test (e.g., Wilcoxon signed-rank) between IDD and the strongest baseline, siMLPe for the caregiver and TCD for the care receiver; if the reported 1.3 to 3.2 mm average advantages are not significant across the 8.7k test sequences, the claim that the interaction-aware model significantly outperforms its baselines collapses.

Watch

Extended reading notes

Core claim

The central claim is that conditioning pose prediction on both interacting agents' observed motion, rather than on one agent alone, captures the coupled dynamics of physical assistance and yields measurably better pose forecasts. On HHI-Assist, the IDD model achieves average per-joint errors of 50.4 mm for the caregiver and 34.3 mm for the care receiver over a one-second horizon, beating the best interaction-unaware baselines (siMLPe at 51.7 mm and TCD at 37.5 mm, respectively). The model also generalizes to the unseen lay-to-stand task, holding average errors near 89 mm and 63 mm. In a controlled 'delayed' variant where one person's observation is shifted by 0.5 s, errors drop further, supporting the interpretation that the gains come from modeling reciprocal influence.

Load-bearing premise

The results depend on the assumption that how two humans move while helping each other is similar enough to how a human and a robot would move together that predictions learned from human-human data will still help a robot assist a person.

Editorial extensions

If this is right

  • Controllers for assistive robots can use IDD-style predictions as a look-ahead signal, smoothing reactions to a care receiver's onset of movement during transfers.
  • The dataset gives future work a fixed benchmark with a train/validation/test split that has no participant overlap, so pose-prediction methods can be compared fairly on physical-assistance motion.
  • A joint-angle-trained variant produces predictions that keep link lengths constant (unlike joint positions, which drift 7 to 15 mm), a property needed if predictions are converted into robot joint commands.
  • The model's lower error on the unseen lay-to-stand task suggests the learned coupling transfers partly across tasks, even though the gap between tasks remains a challenge.

Reading between the lines

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

  • A per-phase error analysis, splitting the horizon into contact onset, lifting, and settling, could reveal whether the interaction-aware advantage concentrates in the moments right after physical contact, which would guide where a robot should pay most attention.
  • If the transferability assumption holds, the dataset can be retargeted to a robot's kinematics and used directly as training data for behavior cloning of assistance motions, an extension the paper names but does not test.
  • The rare 'unconstrained' clips (9 demonstrations) could serve as a domain-randomization prior for robustness training, letting downstream models see more varied motion than the four scripted tasks provide.
  • The measured advantage over the closest baselines is a few millimeters in average MPJPE, so the practical significance for robot controllers is best judged by downstream tasks, such as contact-force prediction, rather than by the raw error gap alone.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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 introduces HHI-Assist, a motion-capture dataset of human-human physical assistance interactions (sit-to-stand, lay-to-sit, lay-to-stand, and unconstrained tasks), and proposes IDD, a conditional Transformer-based denoising diffusion model that predicts future poses of both the caregiver and care receiver conditioned on the observed poses of both agents. The authors evaluate IDD against several baselines on Tasks 1-2, report a generalization experiment on the small Task 3, and study joint-angle versus joint-position representations. The abstract claims improvements over baselines and strong generalization to unseen scenarios, and the dataset and code are publicly released.

Significance. If the claims are supported, HHI-Assist would be a useful new resource: it is, to my knowledge, the first marker-based motion-capture dataset focused specifically on physical assistance scenarios, with a reasonable number of demonstrations from multiple participant pairs. The IDD model is a natural extension of the authors' earlier single-agent diffusion pose predictor, and the dataset-plus-benchmark package with released code could be of value to researchers in physical human-robot interaction, behavior cloning, and human motion forecasting. The paper also contains useful exploratory analyses, including t-SNE task separability and link-length consistency for joint-angle representations. However, the headline claims are currently stronger than the evidence: the main table has no variance or significance testing, and the generalization experiment lacks baseline comparisons. These issues are fixable but are load-bearing for the paper's central message.

major comments (4)
  1. [Section V-D, Table II] The central claim that IDD 'significantly outperforms' the interaction-unaware baselines is not supported by the reported statistics. Table II gives a single MPJPE value per model and horizon with no error bars, confidence intervals, number of seeds, or significance tests. The average margins over TCD are about 1.6 mm for the caregiver and 3.2 mm for the care receiver; with a test set of 8.7k sequences a paired test might well show significance, but the paper does not demonstrate this. Please report variances across seeds or bootstrapped confidence intervals and perform significance tests (or at least state whether the differences are significant), or soften the wording from 'significantly outperforms' to 'achieves lower average MPJPE.'
  2. [Section V-E1] The generalization evaluation does not support the abstract's claim of 'strong generalization to unseen scenarios.' Only IDD absolute errors on Task 3 are reported (average MPJPE 89.3 mm for CG and 62.5 mm for CR), with no comparison to Zero-Vel, Constant-Vel, or any learned baseline. Under distribution shift, trivial predictors can be competitive, so the reader cannot tell whether IDD generalizes better than a constant-velocity model. In addition, Task 3 consists of only 10 demonstrations from a single participant pair, making the conclusion fragile. Please either add baseline comparisons on Task 3 and frame the result as preliminary, or substantially scale up the generalization evaluation.
  3. [Section V-B and V-C] The adaptation of the single-agent baselines to the two-agent interaction setting is underspecified. In particular, TCD is described as a denoising diffusion model for single human pose prediction, and DSTFormer as a dual-stream spatio-temporal Transformer, but the paper does not state how these models receive the concatenated CG/CR observations or how their output heads predict both agents. Without this information the benchmark comparisons cannot be reproduced exactly. Please specify the input/output encoding and any hyperparameter changes for each baseline, including siMLPe.
  4. [Section V-A] The procedure for constructing the 44.8k/3.5k/8.7k train/validation/test sequences from the 908 demonstrations should be described. If sliding windows are used, the stride and the relationship between test sequences and training sequences from the same demonstration clip need to be stated; the no-participant-overlap guarantee does not by itself rule out clip-level overlap between temporally adjacent training and test windows. This is important for the validity of the benchmark numbers in Table II.
minor comments (5)
  1. [Section IV-A] In the notation, 'GR' appears instead of 'CR' as the abbreviation for care receiver.
  2. [Section V-E3] There is a doubled comma in 'MPJPE, , likely'; also, 'around 7% higher average MPJPE' should be clarified as a relative rather than absolute increase.
  3. [References] The baseline referred to as DSTFormer is reference [60], whose title is 'MotionBERT: A Unified Perspective on Learning Human Motion Representations'; the naming should be reconciled in the text and Table II.
  4. [Section III-A] The phrase 'a non-monetary incentive equivalent to 10 USD/h' is awkward; 'USD 10/hour' would be clearer.
  5. [Section V-E2] In the Delayed IDD experiment, the time indices 't=0 to t=1' and 't=0.5 to t=1.5' should be explicitly labeled as seconds (or frames) to avoid ambiguity.

Circularity Check

1 steps flagged · score 2.0 of 10

No central circularity; the one reduction found is an auxiliary oracle experiment (Delayed IDD) whose conditioning window overlaps the prediction target.

  1. self definitional [Section V-E2, 'Delayed Coupled Dynamics']
    "our modified model, called “Delayed IDD”, takes as input the observation sequence of CG/CR from t = 0 to t = 1 and a delayed observation of CR/CG from t = 0.5 to t = 1.5, and is supposed to predict t = 1 to t = 2 of CG/CR. ... The enhanced prediction accuracy for both agents indicate that the Delayed IDD model can better anticipate each agent’s subsequent movements by decoupling their interactions to some extent."

    The prediction target is Y_s = [p_s1, ..., p_sF] for t = 1 to t = 2, but the conditioning input for each agent includes the other agent's ground-truth positions from t = 0.5 to t = 1.5. For the CG prediction, the CR input therefore contains the actual CR future during the first half of the target window (t = 1.0 to t = 1.5). The reported improvement (CG average 47.6 mm vs 50.4 mm; CR 32.5 mm vs 34.3 mm) is partially forced by construction, because the model is given part of the very future it is supposed to predict. The paper does disclose the setup as an information-transfer limit rather than as a deployable predictor, so this is not the central benchmark claim, but the 'enhanced prediction accuracy ... can better anticipate' sentence presents a leakage-induced gain as a modeling insight.

full rationale

The central claim of the paper is that the interaction-aware IDD model 'significantly outperforms the interaction-unaware models and other baselines' on the newly introduced HHI-Assist benchmark. The evidence for this is Table II, which compares IDD against external baselines (SiMLPe, DSTFormer, TCD) and simple kinematic baselines (Zero-Vel, Constant-Vel) on a standard train/validation/test split of the collected data. This is a normal, self-contained benchmarking procedure: the model is trained and evaluated on the same dataset, but that is not circular reasoning. The main self-citation is TCD [44], which is both the architectural starting point and a baseline; it is disclosed, appropriate, and not used as an unverified uniqueness argument or to forbid alternatives. The generalization experiment on Task 3 is under-powered and omits baseline comparisons, and the word 'significantly' is not backed by variance or significance tests; these are statistical-evidence weaknesses, not circularity. The HRI transferability premise is explicitly deferred to future work and is not required for the dataset's internal validity. The only concrete reduction to inputs is the auxiliary 'Delayed IDD' experiment, where the conditioning input overlaps the first 0.5 s of the prediction target, making part of the reported accuracy gain an artifact of the experimental construction. Because this experiment is presented as an information-transfer upper bound rather than as the central contribution, the paper as a whole has no significant circularity; the score of 2 reflects the single, non-central self-referential auxiliary result.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities. The free parameters listed are standard hyperparameters and temporal windows chosen by the authors. The key axiomatic load is the transferability assumption from HHI to HRI, which is explicitly deferred to future work.

free parameters (4)
  • Diffusion steps T = 50
    Chosen by hand for the diffusion process; not fitted to data.
  • Observation window O = 24 timesteps (1 second)
    Design choice for the problem setup, set to 24 at 24 fps.
  • Prediction horizon F = 24 timesteps (1 second)
    Design choice matching receding horizon control settings.
  • Number of transformer blocks = 4
    Architecture hyperparameter, no ablation or tuning shown.
assumptions (3)
  • domain assumption Human-human interaction mocap is representative of human-robot physical assistance.
    Stated in the Introduction: 'We believe this data and the techniques presented in this work are transferable to physical HRI settings and reserve this for future study.' The entire motivation for the dataset rests on this unverified premise.
  • domain assumption The Optitrack 21-joint skeleton model adequately captures the motion relevant to assistance.
    Section III-A describes the skeleton model and calibration to participant link dimensions. The choice of joint granularity is not validated against alternative marker sets.
  • domain assumption Manual exclusion of occluded clips does not bias the dataset.
    Section III-A states that data were excluded upon manual inspection when markers were occluded enough to cause unnatural or infeasible skeleton overlap. This subjective filtering could introduce selection bias, though it is a reasonable quality control step.

how reviews work

0 comments
Cite this review

Pith. "Pith review of HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario." pith.science (2026). https://pith.science/paper/SATHGWRD

@misc{pith2026250910096,
  author       = {Pith},
  title        = {Pith review of: HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SATHGWRD}},
  note         = {Machine review of arXiv:2509.10096}
}
read the original abstract

The increasing labor shortage and aging population underline the need for assistive robots to support human care recipients. To enable safe and responsive assistance, robots require accurate human motion prediction in physical interaction scenarios. However, this remains a challenging task due to the variability of assistive settings and the complexity of coupled dynamics in physical interactions. In this work, we address these challenges through two key contributions: (1) HHI-Assist, a dataset comprising motion capture clips of human-human interactions in assistive tasks; and (2) a conditional Transformer-based denoising diffusion model for predicting the poses of interacting agents. Our model effectively captures the coupled dynamics between caregivers and care receivers, demonstrating improvements over baselines and strong generalization to unseen scenarios. By advancing interaction-aware motion prediction and introducing a new dataset, our work has the potential to significantly enhance robotic assistance policies. The dataset and code are available at: https://sites.google.com/view/hhi-assist/home

Figures

Figures reproduced from arXiv: 2509.10096 by the authors.

Figure 1
Figure 1. Predictions of joint positions for the caregiver (blue [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Examples of physical assistance scenarios from the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The t-SNE plot of the HHI-Assist dataset motions [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: IDD takes the pose sequences of both CG (blue skeleton) and CR (pink skeleton) as input, predicting their corresponding [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: (a) Qualitative comparison of pose predictions from the baselines and our model on the HHI-Assist dataset shown [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Comparison of link length variations in predictions [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

60 extracted references · 58 canonical work pages

  1. [1]

    Demographics and automation

    Daron Acemoglu and Pascual Restrepo. Demographics and automation. The Review of Economic Studies, 89(1):1–44, 06 2021. 1

    work page 2021

  2. [2]

    Socially and contextually aware human motion and pose forecasting.IEEE Robotics and Automation Letters (RA-L), 2020

    Vida Adeli, Ehsan Adeli, Ian Reid, Juan Carlos Niebles, and Hamid Rezatofighi. Socially and contextually aware human motion and pose forecasting.IEEE Robotics and Automation Letters (RA-L), 2020. 2

    work page 2020

  3. [3]

    A spatio-temporal transformer for 3d human motion prediction

    Emre Aksan, Manuel Kaufmann, Peng Cao, and Otmar Hilliges. A spatio-temporal transformer for 3d human motion prediction. InInter- national Conference on 3D Vision. IEEE, 2021. 2

    work page 2021

  4. [4]

    Certified human trajectory prediction

    Mohammadhossein Bahari, Saeed Saadatnejad, Amirhossein Askari Farsangi, Seyed-Mohsen Moosavi-Dezfooli, and Alexandre Alahi. Certified human trajectory prediction. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 2

    work page 2025

  5. [5]

    Belfusion: Latent diffusion for behavior-driven human motion prediction

    German Barquero, Sergio Escalera, and Cristina Palmero. Belfusion: Latent diffusion for behavior-driven human motion prediction. In IEEE/CVF International Conference on Computer Vision (ICCV), 2023. 2

    work page 2023

  6. [6]

    A unified 3d human motion synthesis model via conditional variational auto-encoder

    Yujun Cai, Yiwei Wang, Yiheng Zhu, Tat-Jen Cham, Jianfei Cai, Junsong Yuan, Jun Liu, Chuanxia Zheng, Sijie Yan, Henghui Ding, et al. A unified 3d human motion synthesis model via conditional variational auto-encoder. InIEEE/CVF International Conference on Computer Vision (ICCV), 2021. 2

    work page 2021

  7. [7]

    Relational graph learning for crowd navigation

    Changan Chen, Sha Hu, Payam Nikdel, Greg Mori, and Manolis Savva. Relational graph learning for crowd navigation. InIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),

    work page

  8. [8]

    Crowd- robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning

    Changan Chen, Yuejiang Liu, Sven Kreiss, and Alexandre Alahi. Crowd- robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning. InIEEE International Conference on Robotics and Automation (ICRA), 2019. 2

    work page 2019

Show all 60 references
  1. [9]

    Humanmac: Masked motion completion for human motion prediction

    Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, and Tongliang Liu. Humanmac: Masked motion completion for human motion prediction. InIEEE/CVF International Conference on Computer Vision (ICCV), 2023. 2

    2023

  2. [10]

    Diffusion policy: Visuomotor policy learning via action diffusion

    Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. InProceedings of Robotics: Science and Systems, 2023. 7

    2023

  3. [11]

    Bio- lstm: A biomechanically inspired recurrent neural network for 3-d pedestrian pose and gait prediction.IEEE Robotics and Automation Letters (RA-L), 2019

    Xiaoxiao Du, Ram Vasudevan, and Matthew Johnson-Roberson. Bio- lstm: A biomechanically inspired recurrent neural network for 3-d pedestrian pose and gait prediction.IEEE Robotics and Automation Letters (RA-L), 2019. 2

    2019

  4. [12]

    Action anticipation: Reading the intentions of humans and robots.IEEE Robotics and Automation Letters (RA-L), 2018

    Nuno Ferreira Duarte, Mirko Rakovi ´c, Jovica Tasevski, Moreno Ignazio Coco, Aude Billard, and Jos ´e Santos-Victor. Action anticipation: Reading the intentions of humans and robots.IEEE Robotics and Automation Letters (RA-L), 2018. 2

    2018

  5. [13]

    Reconstructing three-dimensional models of interacting humans.CoRR, 2023

    Mihai Fieraru, Mihai Zanfir, Elisabeta Oneata, Alin-Ionut Popa, Vlad Olaru, and Cristian Sminchisescu. Reconstructing three-dimensional models of interacting humans.CoRR, 2023. 2

    2023

  6. [14]

    Recurrent network models for human dynamics

    Katerina Fragkiadaki, Sergey Levine, Panna Felsen, and Jitendra Malik. Recurrent network models for human dynamics. InIEEE/CVF Interna- tional Conference on Computer Vision (ICCV), 2015. 2

    2015

  7. [15]

    Multi-person extreme motion prediction

    Wen Guo, Xiaoyu Bie, Xavier Alameda-Pineda, and Francesc Moreno- Noguer. Multi-person extreme motion prediction. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2

    2022

  8. [16]

    Back to mlp: A simple baseline for human motion prediction.arXiv:2207.01567, 2022

    Wen Guo, Yuming Du, Xi Shen, Vincent Lepetit, Alameda-Pineda Xavier, and Moreno-Noguer Francesc. Back to mlp: A simple baseline for human motion prediction.arXiv:2207.01567, 2022. 5, 6 8 IEEE ROBOTICS AND AUTOMATION LETTERS. PREPRINT VERSION. ACCEPTED JUNE, 2025

    2022 arXiv

  9. [17]

    Denoising diffusion probabilistic models.arxiv:2006.11239, 2020

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models.arxiv:2006.11239, 2020. 2

    2006 arXiv

  10. [18]

    Motionmap: Representing multimodal- ity in human pose forecasting

    Reyhaneh Hosseininejad, Megh Shukla, Saeed Saadatnejad, Mathieu Salzmann, and Alexandre Alahi. Motionmap: Representing multimodal- ity in human pose forecasting. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025. 2

    2025

  11. [19]

    Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments.IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2014

    Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu. Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments.IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2014. 2

    2014

  12. [20]

    Worldpose: A world cup dataset for global 3d human pose estimation.European Conference on Computer Vision (ECCV), 2024

    Tianjian Jiang, Johsan Billingham, Sebastian M ¨uksch, Juan Zarate, Nicolas Evans, Martin Oswald, Marc Pollefeys, Otmar Hilliges, Manuel Kaufmann, and Jie Song. Worldpose: A world cup dataset for global 3d human pose estimation.European Conference on Computer Vision (ECCV), 2024. 2

    2024

  13. [21]

    Real-time human motion estimation for human robot collaboration

    Jie Kang, Kai Jia, Fang Xu, Fengshan Zou, Yanan Zhang, and Hengle Ren. Real-time human motion estimation for human robot collaboration. InInternational Conference on CYBER Technology in Automation, Control, and Intelligent Systems. IEEE, 2018. 1

    2018

  14. [22]

    Motion diversification networks

    Hee Jae Kim and Eshed Ohn-Bar. Motion diversification networks. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 2

    2024

  15. [23]

    Air-act2act: Human–human interaction dataset for teaching non-verbal social behav- iors to robots.International Journal of Robotics Research, 40(4-5):691– 697, 2021

    Woo-Ri Ko, Minsu Jang, Jaeyeon Lee, and Jaehong Kim. Air-act2act: Human–human interaction dataset for teaching non-verbal social behav- iors to robots.International Journal of Robotics Research, 40(4-5):691– 697, 2021. 2

    2021

  16. [24]

    Whole-body model-predictive control applied to the hrp-2 humanoid

    Jonas Koenemann, Andrea Del Prete, Yuval Tassa, Emanuel Todorov, Olivier Stasse, Maren Bennewitz, and Nicolas Mansard. Whole-body model-predictive control applied to the hrp-2 humanoid. InIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),

  17. [25]

    Bihmp- gan: Bidirectional 3d human motion prediction gan

    Jogendra Nath Kundu, Maharshi Gor, and R Venkatesh Babu. Bihmp- gan: Bidirectional 3d human motion prediction gan. InProceedings of the AAAI conference on artificial intelligence, volume 33, pages 8553– 8560, 2019. 2

    2019

  18. [26]

    Convolutional sequence to sequence model for human dynamics

    Chen Li, Zhen Zhang, Wee Sun Lee, and Gim Hee Lee. Convolutional sequence to sequence model for human dynamics. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 2

    2018

  19. [27]

    Motion prediction using trajectory cues

    Zhenguang Liu, Pengxiang Su, Shuang Wu, Xuanjing Shen, Haipeng Chen, Yanbin Hao, and Meng Wang. Motion prediction using trajectory cues. InIEEE/CVF International Conference on Computer Vision (ICCV), 2021. 2

    2021

  20. [28]

    Progressively generating better initial guesses towards next stages for high-quality human motion prediction

    Tiezheng Ma, Yongwei Nie, Chengjiang Long, Qing Zhang, and Guiqing Li. Progressively generating better initial guesses towards next stages for high-quality human motion prediction. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2

    2022

  21. [29]

    Troje, Gerard Pons- Moll, and Michael J

    Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons- Moll, and Michael J. Black. Amass: Archive of motion capture as surface shapes. InIEEE/CVF International Conference on Computer Vision (ICCV), 2019. 2

    2019

  22. [30]

    Learn- ing trajectory dependencies for human motion prediction

    Wei Mao, Miaomiao Liu, Mathieu Salzmann, and Hongdong Li. Learn- ing trajectory dependencies for human motion prediction. InIEEE/CVF International Conference on Computer Vision (ICCV), 2019. 2

    2019

  23. [31]

    Roberto Martin-Martin, Mihir Patel, Hamid Rezatofighi, Abhijeet Shenoi, JunYoung Gwak, Eric Frankel, Amir Sadeghian, and Silvio Savarese. Jrdb: A dataset and benchmark of egocentric robot visual perception of humans in built environments.IEEE Transactions on Pattern Analysis a...

  24. [32]

    On human motion prediction using recurrent neural networks

    Julieta Martinez, Michael J Black, and Javier Romero. On human motion prediction using recurrent neural networks. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 2

    2017

  25. [33]

    Motion capture file formats explained.Department of Computer Science, University of Sheffield, 211:241–244, 2001

    Maddock Meredith, Steve Maddock, et al. Motion capture file formats explained.Department of Computer Science, University of Sheffield, 211:241–244, 2001. 3

    2001

  26. [34]

    Motion prediction with gaussian processes for safe human-robot interaction in virtual environments.IEEE Access,

    Stanley Mugisha, Vamsi Krishna Guda, Christine Chevallereau, Damien Chablat, and Matteo Zoppi. Motion prediction with gaussian processes for safe human-robot interaction in virtual environments.IEEE Access,

  27. [35]

    Prime 17w

    OptiTrack. Prime 17w. https://optitrack.com/cameras/prime-17w/. Ac- cessed: 2024-06-23. 3

    2024

  28. [36]

    Skeleton marker set: Core (50), 2023

    OptiTrack. Skeleton marker set: Core (50), 2023. 3

    2023

  29. [37]

    Artificial intelligence (ai) and robotics in elderly healthcare: enabling independence and quality of life.Cureus, 15(8),

    Srikanta Padhan, Avilash Mohapatra, Senthil Kumar Ramasamy, and Sanjana Agrawal. Artificial intelligence (ai) and robotics in elderly healthcare: enabling independence and quality of life.Cureus, 15(8),

  30. [38]

    Amp: Adversarial motion priors for stylized physics-based character control.ACM Transactions on Graphics (ToG), 40(4):1–20,

    Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa. Amp: Adversarial motion priors for stylized physics-based character control.ACM Transactions on Graphics (ToG), 40(4):1–20,

  31. [39]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj¨orn Ommer. High-resolution image synthesis with latent diffusion models. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2

    2022

  32. [40]

    Are socially-aware trajectory prediction models really socially- aware?Transportation Research Part C: Emerging Technologies, 2022

    Saeed Saadatnejad, Mohammadhossein Bahari, Pedram Khorsandi, Mo- hammad Saneian, Seyed-Mohsen Moosavi-Dezfooli, and Alexandre Alahi. Are socially-aware trajectory prediction models really socially- aware?Transportation Research Part C: Emerging Technologies, 2022. 2

    2022

  33. [41]

    Social-transmotion: Promptable human trajectory prediction

    Saeed Saadatnejad, Yang Gao, Kaouther Messaoud, and Alexandre Alahi. Social-transmotion: Promptable human trajectory prediction. In International Conference on Learning Representations (ICLR), 2024. 2

    2024

  34. [42]

    Pedestrian 3d bounding box prediction

    Saeed Saadatnejad, Yi Zhou Ju, and Alexandre Alahi. Pedestrian 3d bounding box prediction. InSymposium of the European Association for Research in Transportation, 2022. 2

    2022

  35. [43]

    Toward reliable human pose forecasting with uncertainty.IEEE Robotics and Automation Letters (RA-L), 2024

    Saeed Saadatnejad, Mehrshad Mirmohammadi, Matin Daghyani, Parham Saremi, Yashar Zoroofchi Benisi, Amirhossein Alimohammadi, Zahra Tehraninasab, Taylor Mordan, and Alexandre Alahi. Toward reliable human pose forecasting with uncertainty.IEEE Robotics and Automation Letters (RA-...

    2024

  36. [44]

    A generic diffusion-based approach for 3d human pose prediction in the wild

    Saeed Saadatnejad, Ali Rasekh, Mohammadreza Mofayezi, Yasamin Medghalchi, Sara Rajabzadeh, Taylor Mordan, and Alexandre Alahi. A generic diffusion-based approach for 3d human pose prediction in the wild. InIEEE International Conference on Robotics and Automation (ICRA), 2023. ...

    2023

  37. [45]

    Learning 3-d object orientation from images

    Ashutosh Saxena, Justin Driemeyer, and Andrew Y Ng. Learning 3-d object orientation from images. InIEEE International Conference on Robotics and Automation (ICRA), 2009. 6

    2009

  38. [46]

    Space-time-separable graph convolutional network for pose forecasting

    Theodoros Sofianos, Alessio Sampieri, Luca Franco, and Fabio Galasso. Space-time-separable graph convolutional network for pose forecasting. InIEEE/CVF International Conference on Computer Vision (ICCV), pages 11209–11218, 2021. 2

    2021

  39. [47]

    Social diffusion: Long-term multiple human motion anticipation

    Julian Tanke, Linguang Zhang, Amy Zhao, Chengcheng Tang, Yujun Cai, Lezi Wang, Po-Chen Wu, Juergen Gall, and Cem Keskin. Social diffusion: Long-term multiple human motion anticipation. InIEEE/CVF International Conference on Computer Vision (ICCV), 2023. 2

    2023

  40. [48]

    Csdi: Conditional score-based diffusion models for probabilistic time series imputation

    Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. Csdi: Conditional score-based diffusion models for probabilistic time series imputation. InAdvances in Neural Information Processing Systems (NeurIPS), 2021. 2

    2021

  41. [49]

    Drake: Model-based design and verification for robotics, 2019

    Russ Tedrake and the Drake Development Team. Drake: Model-based design and verification for robotics, 2019. 5

    2019

  42. [50]

    Visualizing data using t-sne.Journal of Machine Learning Research (JMLR), 2008

    Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne.Journal of Machine Learning Research (JMLR), 2008. 3

    2008

  43. [51]

    Human posture prediction during physical human-robot interaction.IEEE Robotics and Automation Letters (RA-L), 2021

    Lorenzo Vianello, Jean-Baptiste Mouret, Eloise Dalin, Alexis Aubry, and Serena Ivaldi. Human posture prediction during physical human-robot interaction.IEEE Robotics and Automation Letters (RA-L), 2021. 2

    2021

  44. [52]

    Recovering accurate 3d human pose in the wild using imus and a moving camera

    Timo von Marcard, Roberto Henschel, Michael J Black, Bodo Rosen- hahn, and Gerard Pons-Moll. Recovering accurate 3d human pose in the wild using imus and a moving camera. InEuropean Conference on Computer Vision (ECCV). Springer, 2018. 2

    2018

  45. [53]

    The pose knows: Video forecasting by generating pose futures

    Jacob Walker, Kenneth Marino, Abhinav Gupta, and Martial Hebert. The pose knows: Video forecasting by generating pose futures. InIEEE/CVF International Conference on Computer Vision (ICCV), 2017. 2

    2017

  46. [54]

    Human motion predic- tion in human-robot handovers based on dynamic movement primitives

    Dominik Widmann and Yiannis Karayiannidis. Human motion predic- tion in human-robot handovers based on dynamic movement primitives. InEuropean Control Conference (ECC). IEEE, 2018. 1

    2018

  47. [55]

    Are robots the solution to the crisis in older-person care? Nature, 2024

    Tammy Worth. Are robots the solution to the crisis in older-person care? Nature, 2024. 1

    2024

  48. [56]

    Hi4d: 4d instance segmentation of close human interaction

    Yifei Yin, Chen Guo, Manuel Kaufmann, Juan Jose Zarate, Jie Song, and Otmar Hilliges. Hi4d: 4d instance segmentation of close human interaction. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2

    2023

  49. [57]

    Dlow: Diversifying latent flows for diverse human motion prediction

    Ye Yuan and Kris Kitani. Dlow: Diversifying latent flows for diverse human motion prediction. InEuropean Conference on Computer Vision (ECCV). Springer, 2020. 2

    2020

  50. [58]

    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. InIEEE/CVF International Conference on Computer Vision (ICCV), 2023. 2

    2023

  51. [59]

    On the continuity of rotation representations in neural networks

    Yi Zhou, Connelly Barnes, Jingwan Lu, Jimei Yang, and Hao Li. On the continuity of rotation representations in neural networks. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 6

    2019

  52. [60]

    Motionbert: A unified perspective on learning human motion representations

    Wentao Zhu, Xiaoxuan Ma, Zhaoyang Liu, Libin Liu, Wayne Wu, and Yizhou Wang. Motionbert: A unified perspective on learning human motion representations. InIEEE/CVF International Conference on Computer Vision (ICCV), 2023. 2, 5, 6

    2023

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.