2026-06-05原文2 min readUpdated: 2026-06-05

Recovering Physically Plausible Human-Object Interactions from Monocular Videos

This paper presents RePHO, a physics-guided reconstruction framework that recovers physically plausible human-object interactions from monocular videos. It starts with a kinematic estimate and refines it via reinforcement learning in a physics simulator, using an adaptive sampling strategy to handle noisy estimates. Results show clear improvements on two benchmarks.

SourcearXiv Computer VisionAuthor: Dingbang Huang, Etienne Vouga, Qixing Huang, Georgios Pavlakos

[2606.05359] Recovering Physically Plausible Human-Object Interactions from Monocular Videos

[Submitted on 3 Jun 2026]

Title:Recovering Physically Plausible Human-Object Interactions from Monocular Videos

View a PDF of the paper titled Recovering Physically Plausible Human-Object Interactions from Monocular Videos, by Dingbang Huang and 2 other authors

View PDF HTML (experimental)

Abstract:In this paper, we propose RePHO, a method to reconstruct physically plausible human-object interactions (HOI) from monocular videos. While existing kinematic-based approaches produce visually plausible motion, they often result in physically implausible artifacts such as interpenetration and object floating. To overcome these issues, we introduce a physics-guided reconstruction framework. We begin with a kinematic estimate and then refine it by training a policy with reinforcement learning (RL). This policy is optimized to reproduce the interaction in a physics simulator. Because kinematic estimates are typically noisy, naive RL training can fail. Therefore, we propose an adaptive sampling strategy with a dual self-updating mechanism that can identify the frames with the most informative and reliable kinematic reconstruction. Our process progressively improves reconstruction quality and yields physically consistent HOI sequences. We demonstrate our approach on two standard HOI benchmarks and achieve clear improvements in physical plausibility metrics over state-of-the-art methods. Project Page: this https URL

Comments: CVPR 2026. Project Page: this https URL

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2606.05359 [cs.CV]

(or arXiv:2606.05359v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.05359

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Dingbang Huang [view email] [v1] Wed, 3 Jun 2026 19:02:48 UTC (3,594 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled Recovering Physically Plausible Human-Object Interactions from Monocular Videos, by Dingbang Huang and 2 other authors

View PDF

HTML (experimental)

TeX Source

view license

Current browse context:

cs.CV

new | recent | 2026-06

Change to browse by:

References & Citations

NASA ADS

Google Scholar

Semantic Scholar

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)