PPR: Physically Plausible Reconstruction from Monocular Videos

Gengshan Yang, Shuo Yang, John Z. Zhang, Zachary Manchester, Deva Ramanan
2/14/2026

No official code found. Best implementation: gengshan-y/ppr (85 stars, MIT, last commit 2024-07-03). 1 more below.

Abstract

Given monocular videos, we build 3D models of articulated objects and environments whose 3D configurations satisfy dynamics and contact constraints. At its core, our method leverages differentiable physics simulation to aid visual reconstructions. We couple differentiable physics simulation with differentiable rendering via coordinate descent, which enables end-to-end optimization of, not only 3D reconstructions, but also physical system parameters from videos. We demonstrate the effectiveness of physics-informed reconstruction on monocular videos of quadruped animals and humans. It reduces reconstruction artifacts (e.g., scale ambiguity, unbalanced poses, and foot swapping) that are challenging to address by visual cues alone, and produces better foot contact estimation.

DOISemantic Scholar

Code Implementations(2)

gengshan-y/pprCommunity69%
MIT

Code for PPR: Physically Plausible Reconstruction from Monocular Videos (ICCV 23).

850May 7, 20232 years agoMIT
4d-reconstructiondifferentiable-renderingdifferentiable-simulationsvideo-reconstruction
MIT

This repo contains the differentiable physics simulation module in "PPR: Physically Plausible Reconstruction from Monocular Videos". ICCV 23.

333Shell, PythonAug 1, 20232 years agoMIT
differentiable-physicsdifferentiable-simulation

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Cite this paper

@article{yang2026ppr,
  title  = {PPR: Physically Plausible Reconstruction from Monocular Videos},
  author = {Gengshan Yang and Shuo Yang and John Z. Zhang and Zachary Manchester and Deva Ramanan},
  year   = {2026},
  doi    = {10.1109/ICCV51070.2023.00362},
  url    = {https://doi.org/10.1109/ICCV51070.2023.00362},
  journal = {ICCV 2023 2023}
}

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