Pith. sign in

REVIEW 3 major objections 2 minor 2 cited by

Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis

T0 review · 3 major / 2 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read Human-X jointly predicts both partners' motions in real time, making human-avatar, human-humanoid, and human-robot interactions physically plausible.

desk verdict Plausible real-time interaction framework, but the corrupted submission makes the technical claims uncheckable; as submitted, not reviewable. read the letter →

arxiv 2508.02106 v1 pith:4IMIQ7OC submitted 2025-08-04 cs.CV cs.RO

Kaiyang Ji , Ye Shi , Zichen Jin , Kangyi Chen , Lan Xu , Yuexin Ma , Jingyi Yu , Jingya Wang This is my paper
classification cs.CVcs.RO
keywords real-timemotionsynthesishumaninteractionauto-regressivediffusionplannerreinforcementlearningphysicalplausibilityhuman-robotvirtualrealitytracking
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 Human-X, a framework for synthesizing physically plausible interaction motion in real time between a human and another entity, such as an avatar, a humanoid, or a robot. Its central claim is that actions and reactions should be predicted jointly at the moment they happen, rather than generated separately and aligned afterward or run through simplified physics. The authors argue that this joint, auto-regressive prediction is what allows the response to stay synchronized with the human partner and remain physically feasible. They report that Human-X outperforms state-of-the-art methods on the Inter-X and InterHuman benchmarks across motion quality, interaction continuity, and physical plausibility, and they demonstrate it in a VR interface for human-robot interaction.

What carries the argument

The central mechanism is the auto-regressive reaction diffusion planner, which generates future motion windows conditioned on observed actor motion and interaction history, paired with an actor-aware motion tracking policy trained by reinforcement learning to execute the planned motion on a body while suppressing foot sliding and penetration. The planner supplies the context-aware, synchronized response; the tracking policy supplies physical realism and safety. Together they replace post-hoc alignment or simplified-physics shortcuts with a single real-time loop.

What would settle it

Run Human-X on a physical humanoid with different limb lengths, joint limits, and actuation latency from the human subjects in Inter-X or InterHuman and measure foot sliding, penetration, and reaction delay; if these metrics degrade to kinematic-baseline levels, the cross-embodiment physical-plausibility claim fails.

Watch

Extended reading notes

Core claim

Human-X predicts both sides of an interaction at once: an auto-regressive reaction diffusion planner generates the next segment of the human's action and the partner's reaction from a short history, and an actor-aware motion tracking policy trained with reinforcement learning turns that planned motion into physically feasible body movement. The key design choice is that the reaction is conditioned on the actor's ongoing motion in real time, so synchronization is built into the generation process rather than repaired afterward. On Inter-X and InterHuman, the paper reports consistent improvements over existing methods on motion quality, interaction continuity, and physical plausibility, and it shows the framework operating in a real VR interface for human-robot collaboration.

Load-bearing premise

The load-bearing premise is that a reaction planner and tracking policy trained on human-human interaction datasets will transfer to humanoid and robot bodies with different kinematics and control dynamics; the paper reports no retraining, domain adaptation, or physical robot experiment for that transfer.

Editorial extensions

If this is right

  • Real-time joint action-reaction prediction removes the need for post-hoc alignment and lets an avatar or robot respond to a human partner while the interaction is still unfolding.
  • The reinforcement-learning-based tracking policy suppresses foot sliding and inter-body penetration, so generated motions satisfy basic physical plausibility without a separate post-processing step.
  • Because the planner is auto-regressive, the same framework can sustain long, context-aware interactions rather than one-off pose sequences.
  • On Inter-X and InterHuman, Human-X reports gains in motion quality, interaction continuity, and physical plausibility over state-of-the-art kinematic and physics-based baselines.
  • A VR interface demo shows the framework running in a real human-robot interaction loop, indicating readiness for deployment in immersive systems.

Reading between the lines

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

  • The paper's real-time claim is demonstrated in a VR interface, not on a physical robot; whether the policy transfers to real humanoid actuators remains an open test.
  • Because the planner is trained only on human-human datasets, extending the same pipeline to non-humanoid robots or avatars with very different kinematics would likely require retraining or a domain-adaptation step.
  • The auto-regressive reaction formulation could be extended from dyadic interaction to multi-agent scenes by chaining pairwise reaction predictions, though the paper does not explore this.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The paper proposes Human-X, a framework for real-time synthesis of physically plausible human interaction motion across human-avatar, human-humanoid, and human-robot settings. It combines an auto-regressive reaction diffusion planner that jointly predicts actions and reactions with an actor-aware motion tracking policy trained via reinforcement learning to improve physical realism and avoid artifacts. The abstract claims significant improvements over state-of-the-art methods on the Inter-X and InterHuman datasets and reports validation through a VR interface for human-robot interaction. However, the submitted full text is almost entirely corrupted and unreadable beyond the abstract; no equations, tables, evaluation protocols, quantitative results, or ablations are recoverable, so the methodological and experimental content cannot be inspected.

Significance. If the claims are substantiated, the framework would address a real and important tension between real-time responsiveness and physical plausibility in interactive motion synthesis, and the joint action-reaction formulation could be a useful contribution to human-machine interaction research. The paper also targets concrete applications in VR/AR and human-robot collaboration, which increases its potential impact. However, because the submission prevents verification of the method and its experimental evidence, the significance cannot currently be confirmed. No code or machine-checked proofs are provided, and the claims rest entirely on the abstract, which is not sufficient for a soundness assessment.

major comments (3)
  1. [Full Text (all sections)] The main body of the manuscript is corrupted and unreadable: after the abstract, the text consists of encoding artifacts with no recoverable equations, tables, or evaluation details. As a result, the central claims of real-time performance, physical plausibility, and state-of-the-art accuracy cannot be verified or reproduced from the submission. This is a load-bearing issue because the reviewer cannot inspect the proposed planner, the tracking policy, the reward design, or the experimental methodology.
  2. [Abstract, Experiments] The abstract claims 'significant improvements in motion quality, interaction continuity, and physical plausibility over state-of-the-art methods on Inter-X and InterHuman,' but no numerical metrics, standard deviations, runtime measurements, or ablations are reported in any readable portion of the manuscript. Because the full text is corrupted, these quantitative claims are currently unsupported. A readable manuscript with detailed results and ablations is required before the central claim can be evaluated.
  3. [Abstract, Human-Robot Generalization] The framework is presented as covering human-avatar, human-humanoid, and human-robot systems, but the described experiments are limited to human-human interaction datasets (Inter-X and InterHuman) and a VR interface for human-robot interaction. No physical robot experiments, domain adaptation, or retraining for non-human morphologies are described. The transfer of the learned reaction planner and tracking policy to humanoid or robot kinematics with different joint limits, mass properties, actuation delays, or control frequencies is therefore unverified and constitutes a load-bearing gap for the general claim of real-time physically plausible interaction across diverse entities.
minor comments (2)
  1. [Abstract] The term 'auto-regressive reaction diffusion planner' is introduced without a formal definition or reference in the abstract; if a readable full text is provided, please define the components and the diffusion formulation clearly.
  2. [Abstract] The VR interface is mentioned as validation, but no user study details, latency figures, or task descriptions are given; these should be added to support the 'real-world applications' claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No detectable circularity in the available text; the readable abstract states contributions without exposing any derivation chain that reduces to its own inputs.

full rationale

The submitted full text is corrupted mojibake; no equations, no experimental protocols, no metric definitions, and no self-citation chains are readable. The abstract describes an auto-regressive reaction diffusion planner and an RL-based tracking policy, but it does not specify any loss, reward, or evaluation quantity, so it is impossible to exhibit a fitted parameter being renamed as a prediction or an equation that is definitionally equivalent to an input. There is also no quoted uniqueness theorem or prior-work citation that is load-bearing in the visible text. Per the hard rules, circularity may only be claimed when the paper's own equations or self-citations exhibit the reduction; without readable derivations or benchmarks, no such reduction can be identified. The absence of verifiable evidence is a correctness/verifiability concern, not a demonstrated circularity. Therefore the honest finding is no significant circularity, score 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

Only the abstract was machine-readable in the supplied text, so the ledger captures assumptions explicitly or implicitly stated in the abstract. No free parameters or invented entities are visible at this level.

assumptions (2)
  • domain assumption Inter-X and InterHuman datasets contain sufficient interaction diversity and quality to learn a generalizable reaction planner.
    The method is trained and evaluated on these datasets, but the abstract does not analyze morphological mismatch between human-human data and humanoid/robot bodies.
  • domain assumption The physics simulation used for the RL tracking policy faithfully represents real-world contact, friction, and penetration constraints.
    Physical plausibility claims rest on this simulator being a faithful proxy for real safety constraints; the abstract reports no ablation of simulation-to-real transfer.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis." pith.science (2026). https://pith.science/paper/4IMIQ7OC

@misc{pith2026250802106,
  author       = {Pith},
  title        = {Pith review of: Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4IMIQ7OC}},
  note         = {Machine review of arXiv:2508.02106}
}
read the original abstract

Real-time synthesis of physically plausible human interactions remains a critical challenge for immersive VR/AR systems and humanoid robotics. While existing methods demonstrate progress in kinematic motion generation, they often fail to address the fundamental tension between real-time responsiveness, physical feasibility, and safety requirements in dynamic human-machine interactions. We introduce Human-X, a novel framework designed to enable immersive and physically plausible human interactions across diverse entities, including human-avatar, human-humanoid, and human-robot systems. Unlike existing approaches that focus on post-hoc alignment or simplified physics, our method jointly predicts actions and reactions in real-time using an auto-regressive reaction diffusion planner, ensuring seamless synchronization and context-aware responses. To enhance physical realism and safety, we integrate an actor-aware motion tracking policy trained with reinforcement learning, which dynamically adapts to interaction partners' movements while avoiding artifacts like foot sliding and penetration. Extensive experiments on the Inter-X and InterHuman datasets demonstrate significant improvements in motion quality, interaction continuity, and physical plausibility over state-of-the-art methods. Our framework is validated in real-world applications, including virtual reality interface for human-robot interaction, showcasing its potential for advancing human-robot collaboration.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

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

  1. TROPHIES: Temporal Reconstruction of Places, Humans, and Cameras from Multi-view Videos

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    TROPHIES introduces a unified framework for human-scene-camera reconstruction from multi-view videos, achieving globally aligned and physically plausible 4D outputs on EgoHuman and EgoExo4D.

  2. HAT-4D: Lifting Monocular Video for 4D Multi-Object Interactions via Human-Agent Collaboration

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    HAT-4D presents an agentic VLM-plus-human-in-the-loop pipeline for monocular 4D multi-object interaction reconstruction and releases the MVOIK-4D benchmark.

Reference graph

Works this paper leans on

76 extracted references · 44 canonical work pages · cited by 2 Pith papers

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

  2. [2]

    Gpt-4 technical report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023

    arXiv 2023

  3. [3]

    Listen, denoise, action! audio-driven motion synthesis with diffusion models

    Simon Alexanderson, Rajmund Nagy, Jonas Beskow, and Gustav Eje Henter. Listen, denoise, action! audio-driven motion synthesis with diffusion models. ACM Transactions on Graphics (TOG), 42 0 (4): 0 1--20, 2023

    work page 2023

  4. [4]

    Scheduled sampling for sequence prediction with recurrent neural networks

    Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. Scheduled sampling for sequence prediction with recurrent neural networks. Advances in neural information processing systems, 28, 2015

    2015

  5. [5]

    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

    work page 2024

  6. [6]

    Symbiosim: Human-in-the-loop simulation platform for bidirectional continuing learning in human-robot interaction

    Haoran Chen, Yiteng Xu, Yiming Ren, Yaoqin Ye, Xinran Li, Ning Ding, Peishan Cong, Ziyi Wang, Bushi Liu, Yuhan Chen, et al. Symbiosim: Human-in-the-loop simulation platform for bidirectional continuing learning in human-robot interaction. arXiv preprint arXiv:2502.07358, 2025 a

    work page arXiv 2025

  7. [7]

    Rhino: Learning real-time humanoid-human-object interaction from human demonstrations

    Jingxiao Chen, Xinyao Li, Jiahang Cao, Zhengbang Zhu, Wentao Dong, Minghuan Liu, Ying Wen, Yong Yu, Liqing Zhang, and Weinan Zhang. Rhino: Learning real-time humanoid-human-object interaction from human demonstrations. arXiv preprint arXiv:2502.13134, 2025 b

    arXiv 2025

  8. [8]

    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. In ACM SIGGRAPH 2024 Conference Papers, pages 1--10, 2024

    work page 2024

Show all 76 references
  1. [9]

    Interaction transformer for human reaction generation

    Baptiste Chopin, Hao Tang, Naima Otberdout, Mohamed Daoudi, and Nicu Sebe. Interaction transformer for human reaction generation. IEEE Transactions on Multimedia, 25: 0 8842--8854, 2023

    2023

  2. [10]

    Human-object interaction via automatically designed vlm-guided motion policy

    Zekai Deng, Ye Shi, Kaiyang Ji, Lan Xu, Shaoli Huang, and Jingya Wang. Human-object interaction via automatically designed vlm-guided motion policy. arXiv preprint arXiv:2503.18349, 2025

    2025

  3. [11]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human langu...

    2019

  4. [12]

    Palm-e: an embodied multimodal language model

    Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al. Palm-e: an embodied multimodal language model. In Proceedings of the 40th International Conference on Machine Learning, pages ...

    2023

  5. [13]

    Avatars grow legs: Generating smooth human motion from sparse tracking inputs with diffusion model

    Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, and Artsiom Sanakoyeu. Avatars grow legs: Generating smooth human motion from sparse tracking inputs with diffusion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition...

    2023

  6. [14]

    Generative adversarial nets

    Ian 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

  7. [15]

    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

    2022

  8. [16]

    Omnih2o: Universal and dexterous human-to-humanoid whole-body teleoperation and learning

    Tairan He, Zhengyi Luo, Xialin He, Wenli Xiao, Chong Zhang, Weinan Zhang, Kris Kitani, Changliu Liu, and Guanya Shi. Omnih2o: Universal and dexterous human-to-humanoid whole-body teleoperation and learning. arXiv preprint arXiv:2406.08858, 2024 a

    2024 arXiv

  9. [17]

    Learning human-to-humanoid real-time whole-body teleoperation, 2024 b

    Tairan He, Zhengyi Luo, Wenli Xiao, Chong Zhang, Kris Kitani, Changliu Liu, and Guanya Shi. Learning human-to-humanoid real-time whole-body teleoperation, 2024 b

    2024

  10. [18]

    Gans trained by a two time-scale update rule converge to a local nash equilibrium

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017

    2017

  11. [19]

    Generative adversarial imitation learning

    Jonathan Ho and Stefano Ermon. Generative adversarial imitation learning. Advances in neural information processing systems, 29, 2016

    2016

  12. [20]

    Classifier-free diffusion guidance

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

    2022 arXiv

  13. [21]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33: 0 6840--6851, 2020

    2020

  14. [22]

    Video diffusion models

    Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. Advances in Neural Information Processing Systems, 35: 0 8633--8646, 2022

    2022

  15. [23]

    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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16750--16761, 2023

    2023

  16. [24]

    Intermask: 3d human interaction generation via collaborative masked modelling

    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

  17. [25]

    Exbody2: Advanced expressive humanoid whole-body control

    Mazeyu Ji, Xuanbin Peng, Fangchen Liu, Jialong Li, Ge Yang, Xuxin Cheng, and Xiaolong Wang. Exbody2: Advanced expressive humanoid whole-body control. arXiv preprint arXiv:2412.13196, 2024

    2024 arXiv

  18. [26]

    Motiongpt: Human motion as a foreign language

    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: 0 20067--20079, 2023

    2023

  19. [27]

    Solami: Social vision-language-action modeling for immersive interaction with 3d autonomous characters

    Jianping Jiang, Weiye Xiao, Zhengyu Lin, Huaizhong Zhang, Tianxiang Ren, Yang Gao, Zhiqian Lin, Zhongang Cai, Lei Yang, and Ziwei Liu. Solami: Social vision-language-action modeling for immersive interaction with 3d autonomous characters. arXiv preprint arXiv:2412.00174, 2024

    2024 arXiv

  20. [28]

    Arflow: Human action-reaction flow matching with physical guidance

    Wentao Jiang, Jingya Wang, Kaiyang Ji, Baoxiong Jia, Siyuan Huang, and Ye Shi. Arflow: Human action-reaction flow matching with physical guidance. arXiv preprint arXiv:2503.16973, 2025

    2025 arXiv

  21. [29]

    Auto-encoding variational bayes, 2013

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

    2013

  22. [30]

    Ego-body pose estimation via ego-head pose estimation

    Jiaman Li, Karen Liu, and Jiajun Wu. Ego-body pose estimation via ego-head pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17142--17151, 2023

    2023

  23. [31]

    Hybrik-x: Hybrid analytical-neural inverse kinematics for whole-body mesh recovery

    Jiefeng Li, Siyuan Bian, Chao Xu, Zhicun Chen, Lixin Yang, and Cewu Lu. Hybrik-x: Hybrid analytical-neural inverse kinematics for whole-body mesh recovery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

    2025

  24. [32]

    Interdance: Reactive 3d dance generation with realistic duet interactions

    Ronghui Li, Youliang Zhang, Yachao Zhang, Yuxiang Zhang, Mingyang Su, Jie Guo, Ziwei Liu, Yebin Liu, and Xiu Li. Interdance: Reactive 3d dance generation with realistic duet interactions. arXiv preprint arXiv:2412.16982, 2024

    2024 arXiv

  25. [33]

    Intergen: Diffusion-based multi-human motion generation under complex interactions

    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

  26. [34]

    Chathuman: Language-driven 3d human understanding with retrieval-augmented tool reasoning

    Jing Lin, Yao Feng, Weiyang Liu, and Michael J Black. Chathuman: Language-driven 3d human understanding with retrieval-augmented tool reasoning. arXiv preprint arXiv:2405.04533, 2024

    2024 arXiv

  27. [35]

    Goal-conditioned reinforcement learning: Problems and solutions, 2022

    Minghuan Liu, Menghui Zhu, and Weinan Zhang. Goal-conditioned reinforcement learning: Problems and solutions, 2022

    2022

  28. [36]

    Interactive humanoid: Online full-body motion reaction synthesis with social affordance canonicalization and forecasting

    Yunze Liu, Changxi Chen, and Li Yi. Interactive humanoid: Online full-body motion reaction synthesis with social affordance canonicalization and forecasting. arXiv preprint arXiv:2312.08983, 2023

    2023 arXiv

  29. [37]

    Physreaction: Physically plausible real-time humanoid reaction synthesis via forward dynamics guided 4d imitation

    Yunze Liu, Changxi Chen, Chenjing Ding, and Li Yi. Physreaction: Physically plausible real-time humanoid reaction synthesis via forward dynamics guided 4d imitation. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 3771--3780, 2024

    2024

  30. [38]

    Smpl: A skinned multi-person linear model

    Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. Smpl: A skinned multi-person linear model. ACM Transactions on Graphics, 34 0 (6), 2015

    2015

  31. [39]

    Perpetual humanoid control for real-time simulated avatars

    Zhengyi Luo, Jinkun Cao, Kris Kitani, Weipeng Xu, et al. Perpetual humanoid control for real-time simulated avatars. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10895--10904, 2023 a

    2023

  32. [40]

    Universal humanoid motion representations for physics-based control

    Zhengyi Luo, Jinkun Cao, Josh Merel, Alexander Winkler, Jing Huang, Kris Kitani, and Weipeng Xu. Universal humanoid motion representations for physics-based control. arXiv preprint arXiv:2310.04582, 2023 b

    2023 arXiv

  33. [41]

    Real-time simulated avatar from head-mounted sensors

    Zhengyi Luo, Jinkun Cao, Rawal Khirodkar, Alexander Winkler, Kris Kitani, and Weipeng Xu. Real-time simulated avatar from head-mounted sensors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 571--581, 2024

    2024

  34. [42]

    Isaac gym: High performance gpu-based physics simulation for robot learning

    Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, et al. Isaac gym: High performance gpu-based physics simulation for robot learning. arXiv preprint arXiv:2108.10470, 2021

    2021 arXiv

  35. [43]

    Using apple vision pro to train and control robots, 2024

    Younghyo Park and Pulkit Agrawal. Using apple vision pro to train and control robots, 2024

    2024

  36. [44]

    Expressive body capture: 3d hands, face, and body from a single image

    Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3d hands, face, and body from a single image. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognitio...

    2019

  37. [45]

    Scalable diffusion models with transformers

    William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 4195--4205, 2023

    2023

  38. [46]

    Deepmimic: Example-guided deep reinforcement learning of physics-based character skills

    Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel Van de Panne. Deepmimic: Example-guided deep reinforcement learning of physics-based character skills. ACM Transactions On Graphics (TOG), 37 0 (4): 0 1--14, 2018

    2018

  39. [47]

    Amp: Adversarial motion priors for stylized physics-based character control

    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 0 (4): 0 1--20, 2021

    2021

  40. [48]

    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. In International conference on machine learning, pa...

    2021

  41. [49]

    Humor: 3d human motion model for robust pose estimation

    Davis Rempe, Tolga Birdal, Aaron Hertzmann, Jimei Yang, Srinath Sridhar, and Leonidas J Guibas. Humor: 3d human motion model for robust pose estimation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 11488--11499, 2021

    2021

  42. [50]

    Unitree h1 robot

    Unitree Robotics. Unitree h1 robot. https://www.unitree.com, 2021. Accessed: 2023-10-10

    2021

  43. [51]

    Javier Romero, Dimitrios Tzionas, and Michael J. Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36 0 (6), 2017

    2017

  44. [52]

    Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter

    Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019

    1910 arXiv

  45. [53]

    Duolando: Follower gpt with off-policy reinforcement learning for dance accompaniment

    Li Siyao, Tianpei Gu, Zhitao Yang, Zhengyu Lin, Ziwei Liu, Henghui Ding, Lei Yang, and Chen Change Loy. Duolando: Follower gpt with off-policy reinforcement learning for dance accompaniment. arXiv preprint arXiv:2403.18811, 2024

    2024 arXiv

  46. [54]

    Denoising diffusion implicit models

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

    2010 arXiv

  47. [55]

    Local motion phases for learning multi-contact character movements

    Sebastian Starke, Yiwei Zhao, Taku Komura, and Kazi Zaman. Local motion phases for learning multi-contact character movements. ACM Transactions on Graphics (TOG), 39 0 (4): 0 54--1, 2020

    2020

  48. [56]

    Think then react: Towards unconstrained action-to-reaction motion generation

    Wenhui Tan, Boyuan Li, Chuhao Jin, Wenbing Huang, Xiting Wang, and Ruihua Song. Think then react: Towards unconstrained action-to-reaction motion generation. In The Thirteenth International Conference on Learning Representations, 2025

    2025

  49. [57]

    Role-aware interaction generation from textual description

    Mikihiro Tanaka and Kent Fujiwara. Role-aware interaction generation from textual description. In Proceedings of the IEEE/CVF international conference on computer vision, pages 15999--16009, 2023

    2023

  50. [58]

    A unified diffusion framework for scene-aware human motion estimation from sparse signals

    Jiangnan Tang, Jingya Wang, Kaiyang Ji, Lan Xu, Jingyi Yu, and Ye Shi. A unified diffusion framework for scene-aware human motion estimation from sparse signals. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21251--21262, 2024

    2024

  51. [59]

    Human motion diffusion model

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

    2023

  52. [60]

    Closd: Closing the loop between simulation and diffusion for multi-task character control

    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

  53. [61]

    Gaze-guided hand-object interaction synthesis: Dataset and method

    Jie Tian, Ran Ji, Lingxiao Yang, Suting Ni, Yuexin Ma, Lan Xu, Jingyi Yu, Ye Shi, and Jingya Wang. Gaze-guided hand-object interaction synthesis: Dataset and method. arXiv preprint arXiv:2403.16169, 2024

    2024

  54. [62]

    Edge: Editable dance generation from music

    Jonathan Tseng, Rodrigo Castellon, and Karen Liu. Edge: Editable dance generation from music. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 448--458, 2023

    2023

  55. [63]

    Thor: Text to human-object interaction diffusion via relation intervention

    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 a

    2024 arXiv

  56. [64]

    Human-object interaction from human-level instructions

    Zhen Wu, Jiaman Li, and C Karen Liu. Human-object interaction from human-level instructions. CoRR, 2024 b

    2024

  57. [65]

    Actformer: A gan-based transformer towards general action-conditioned 3d human motion generation

    Liang Xu, Ziyang Song, Dongliang Wang, Jing Su, Zhicheng Fang, Chenjing Ding, Weihao Gan, Yichao Yan, Xin Jin, Xiaokang Yang, et al. Actformer: A gan-based transformer towards general action-conditioned 3d human motion generation. In Proceedings of the IEEE/CVF International C...

    2023

  58. [66]

    Inter-x: Towards versatile human-human interaction 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 interaction analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pag...

    2024

  59. [67]

    Regennet: Towards human action-reaction synthesis

    Liang Xu, Yizhou Zhou, Yichao Yan, Xin Jin, Wenhan Zhu, Fengyun Rao, Xiaokang Yang, and Wenjun Zeng. Regennet: Towards human action-reaction synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1759--1769, 2024 b

    2024

  60. [68]

    Guidance with spherical gaussian constraint for conditional diffusion

    Lingxiao Yang, Shutong Ding, Yifan Cai, Jingyi Yu, Jingya Wang, and Ye Shi. Guidance with spherical gaussian constraint for conditional diffusion. In Proceedings of the 41st International Conference on Machine Learning, pages 56071--56095, 2024

    2024

  61. [69]

    Learning physically simulated tennis skills from broadcast videos

    Ye Yuan, Viktor Makoviychuk, Y Guo, S Fidler, XB Peng, and K Fatahalian. Learning physically simulated tennis skills from broadcast videos. ACM Trans. Graph, 42 0 (4), 2023 a

    2023

  62. [70]

    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. In Proceedings of the IEEE/CVF international conference on computer vision, pages 16010--16021, 2023 b

    2023

  63. [71]

    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

  64. [72]

    Motiondiffuse: Text-driven human motion generation with diffusion model

    Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu. Motiondiffuse: Text-driven human motion generation with diffusion model. IEEE transactions on pattern analysis and machine intelligence, 46 0 (6): 0 4115--4128, 2024

    2024

  65. [73]

    Openhoi: Open-world hand-object interaction synthesis with multimodal large language model

    Zhenhao Zhang, Ye Shi, Lingxiao Yang, Suting Ni, Qi Ye, and Jingya Wang. Openhoi: Open-world hand-object interaction synthesis with multimodal large language model. arXiv preprint arXiv:2505.18947, 2025

    2025

  66. [74]

    Dart: A diffusion-based autoregressive motion model for real-time text-driven motion control

    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

  67. [75]

    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. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5745--5753, 2019

    2019

  68. [76]

    Unidb: A unified diffusion bridge framework via stochastic optimal control

    Kaizhen Zhu, Mokai Pan, Yuexin Ma, Yanwei Fu, Jingyi Yu, Jingya Wang, and Ye Shi. Unidb: A unified diffusion bridge framework via stochastic optimal control. arXiv preprint arXiv:2502.05749, 2025

    2025 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.