{"slug": "recovering-physically-plausible-human-object-interactions-from-monocular-videos", "title": "Recovering Physically Plausible Human-Object Interactions from Monocular Videos", "summary": "Researchers have developed RePHO, a method that reconstructs physically plausible human-object interactions from monocular videos by combining kinematic estimates with reinforcement learning. The approach addresses common artifacts like interpenetration and object floating by training a policy in a physics simulator, using an adaptive sampling strategy to handle noisy kinematic data. Tested on standard benchmarks, RePHO achieves significant improvements in physical plausibility over existing methods.", "body_md": "arXiv:2606.05359v1 Announce Type: new\nAbstract: 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: https://dingbang777.github.io/RePHO/", "url": "https://wpnews.pro/news/recovering-physically-plausible-human-object-interactions-from-monocular-videos", "canonical_source": "https://arxiv.org/abs/2606.05359", "published_at": "2026-06-05 04:00:00+00:00", "updated_at": "2026-06-05 04:17:33.090294+00:00", "lang": "en", "topics": ["computer-vision", "artificial-intelligence", "machine-learning", "robotics"], "entities": ["RePHO", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/recovering-physically-plausible-human-object-interactions-from-monocular-videos", "markdown": "https://wpnews.pro/news/recovering-physically-plausible-human-object-interactions-from-monocular-videos.md", "text": "https://wpnews.pro/news/recovering-physically-plausible-human-object-interactions-from-monocular-videos.txt", "jsonld": "https://wpnews.pro/news/recovering-physically-plausible-human-object-interactions-from-monocular-videos.jsonld"}}