MPMAvatar: Learning 3D Gaussian Avatars with Accurate and Robust Physics-Based Dynamics

• URL: http://arxiv.org/abs/2510.01619v1
• Date: Thu, 02 Oct 2025 02:51:45 GMT
• Title: MPMAvatar: Learning 3D Gaussian Avatars with Accurate and Robust Physics-Based Dynamics
• Authors: Changmin Lee, Jihyun Lee, Tae-Kyun Kim,
• Abstract summary: MPMAvatar is a framework for creating 3D human avatars from multi-view videos.<n>For accurate and robust dynamics modeling, our key idea is to use a Material Point Method-based simulator.
• Score: 27.63650397876897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While there has been significant progress in the field of 3D avatar creation from visual observations, modeling physically plausible dynamics of humans with loose garments remains a challenging problem. Although a few existing works address this problem by leveraging physical simulation, they suffer from limited accuracy or robustness to novel animation inputs. In this work, we present MPMAvatar, a framework for creating 3D human avatars from multi-view videos that supports highly realistic, robust animation, as well as photorealistic rendering from free viewpoints. For accurate and robust dynamics modeling, our key idea is to use a Material Point Method-based simulator, which we carefully tailor to model garments with complex deformations and contact with the underlying body by incorporating an anisotropic constitutive model and a novel collision handling algorithm. We combine this dynamics modeling scheme with our canonical avatar that can be rendered using 3D Gaussian Splatting with quasi-shadowing, enabling high-fidelity rendering for physically realistic animations. In our experiments, we demonstrate that MPMAvatar significantly outperforms the existing state-of-the-art physics-based avatar in terms of (1) dynamics modeling accuracy, (2) rendering accuracy, and (3) robustness and efficiency. Additionally, we present a novel application in which our avatar generalizes to unseen interactions in a zero-shot manner-which was not achievable with previous learning-based methods due to their limited simulation generalizability. Our project page is at: https://KAISTChangmin.github.io/MPMAvatar/

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