ReconPhys: Reconstruct Appearance and Physical Attributes from Single Video
Reconstructing non-rigid objects with physical plausibility remains a significant challenge. Existing approaches leverage differentiable rendering for per-scene optimization, recovering geometry and dynamics but requiring expensive tuning or manual annotation, which limits practicality and generalizability. To address this, we propose ReconPhys, the first feedforward framework that jointly learns physical attribute estimation and 3D Gaussian Splatting reconstruction from a single monocular video. Our method employs a dual-branch architecture trained via a self-supervised strategy, eliminating the need for ground-truth physics labels. Given a video sequence, ReconPhys simultaneously infers geometry, appearance, and physical attributes. Experiments on a large-scale synthetic dataset demonstrate superior performance: our method achieves 21.64 PSNR in future prediction compared to 13.27 by state-of-the-art optimization baselines, while reducing Chamfer Distance from 0.349 to 0.004. Crucially, ReconPhys enables fast inference (<1 second) versus hours required by existing methods, facilitating rapid generation of simulation-ready assets for robotics and graphics.
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Boyuan Wang
claimed_verified
2026-04-15T10:01:44.516Z
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BY.W
chuanshuogushi
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chuanshuogushi
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Xiaofeng Wang
claimed_verified
2026-04-16T08:02:55.079Z
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JeffWang
Jeff-Wang
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Jeff-Wang
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Yongkang Li
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Zheng Zhu
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Yifan Chang
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Angen Ye
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Guosheng Zhao
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Chaojun Ni
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Guan Huang
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Yijie Ren
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Yueqi Duan
69df2c99d3c84fcbf2921927
Xingang Wang
ReconPhys: Reconstruct Appearance and Physical Attributes from Single Video
Reconstructing non-rigid objects with physical plausibility remains a significant challenge. Existing approaches leverage differentiable rendering for per-scene optimization, recovering geometry and dynamics but requiring expensive tuning or manual annotation, which limits practicality and generalizability. To address this, we propose ReconPhys, the first feedforward framework that jointly learns physical attribute estimation and 3D Gaussian Splatting reconstruction from a single monocular video. Our method employs a dual-branch architecture trained via a self-supervised strategy, eliminating the need for ground-truth physics labels. Given a video sequence, ReconPhys simultaneously infers geometry, appearance, and physical attributes. Experiments on a large-scale synthetic dataset demonstrate superior performance: our method achieves 21.64 PSNR in future prediction compared to 13.27 by state-of-the-art optimization baselines, while reducing Chamfer Distance from 0.349 to 0.004. Crucially, ReconPhys enables fast inference (<1 second) versus hours required by existing methods, facilitating rapid generation of simulation-ready assets for robotics and graphics.
ReconPhys presents a feedforward framework that jointly learns physical attribute estimation and 3D Gaussian Splatting reconstruction from monocular video, achieving faster inference and better reconstruction quality than existing optimization-based methods.
3D Gaussian Splatting monocular video physical attribute estimation feedforward framework dual-branch architecture self-supervised strategy differentiable rendering per-scene optimization simulation-ready assets