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PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations

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arxiv 2404.04421 v2 pith:XMP6UU2B submitted 2024-04-05 cs.GR cs.CV

Yang Zheng , Qingqing Zhao , Guandao Yang , Wang Yifan , Donglai Xiang , Florian Dubost , Dmitry Lagun , Thabo Beeler
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Federico Tombari Leonidas Guibas Gordon Wetzstein
This is my paper · ORCID
classification cs.GRcs.CV
keywords physavatarinversephysicsavatarsestimaterenderingclothesdata
verification ladder T0 review T1 audit T2 compute T3 formal
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Modeling and rendering photorealistic avatars is of crucial importance in many applications. Existing methods that build a 3D avatar from visual observations, however, struggle to reconstruct clothed humans. We introduce PhysAvatar, a novel framework that combines inverse rendering with inverse physics to automatically estimate the shape and appearance of a human from multi-view video data along with the physical parameters of the fabric of their clothes. For this purpose, we adopt a mesh-aligned 4D Gaussian technique for spatio-temporal mesh tracking as well as a physically based inverse renderer to estimate the intrinsic material properties. PhysAvatar integrates a physics simulator to estimate the physical parameters of the garments using gradient-based optimization in a principled manner. These novel capabilities enable PhysAvatar to create high-quality novel-view renderings of avatars dressed in loose-fitting clothes under motions and lighting conditions not seen in the training data. This marks a significant advancement towards modeling photorealistic digital humans using physically based inverse rendering with physics in the loop. Our project website is at: https://qingqing-zhao.github.io/PhysAvatar

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Cited by 5 Pith papers

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

  1. AIpparel: A Multimodal Foundation Model for Digital Garments

    cs.CV 2024-12 conditional novelty 7.0 of 10

    AIpparel fine-tunes a large multimodal model to generate and edit sewing patterns from text and images, outperforming prior single-modality methods.

  2. GauSTAR: Gaussian Surface Tracking and Reconstruction

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A Gaussian-on-mesh representation with adaptive unbinding and re-meshing achieves best-on-reported-sequences dynamic surface reconstruction, rendering, and tracking under topology changes.

  3. PBDyG: Position Based Dynamic Gaussians for Motion-Aware Clothed Human Avatars

    cs.CV 2024-12 reject novelty 6.0 of 10

    PBDyG reconstructs animatable human avatars from video by simulating loose clothing with physics, estimating fabric properties from the recorded motion.

  4. Sequential Gaussian Avatars with Hierarchical Motion Context

    cs.CV 2024-11 conditional novelty 5.0 of 10

    A 3D Gaussian avatar model that conditions non-rigid deformation on hierarchical skeleton and vertex motion reaches state-of-the-art rendering quality on three human-capture datasets.

  5. GGAvatar: Reconstructing Garment-Separated 3D Gaussian Splatting Avatars from Monocular Video

    cs.CV 2024-11 conditional novelty 5.0 of 10

    GGAvatar reconstructs a garment-separated 3D Gaussian avatar from a monocular video, supporting novel views, novel poses, clothing transfer, and color editing.

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