DiffXPBD

Author:

Stuyck Tuur1ORCID,Chen Hsiao-yu1ORCID

Affiliation:

1. Meta Reality Labs Research, USA

Abstract

We present DiffXPBD, a novel and efficient analytical formulation for the differentiable position-based simulation of compliant constrained dynamics (XPBD). Our proposed method allows computation of gradients of numerous parameters with respect to a goal function simultaneously leveraging a performant simulation model. The method is efficient, thus enabling differentiable simulations of high resolution geometries and degrees of freedom (DoFs). Collisions are naturally included in the framework. Our differentiable model allows a user to easily add additional optimization variables. Every control variable gradient requires the computation of only a few partial derivatives which can be computed using automatic differentiation code. We demonstrate the efficacy of the method with examples such as elastic cloth and volumetric material parameter estimation, initial value optimization, optimizing for underlying body shape and pose by only observing the clothing, and optimizing a time-varying external force sequence to match sparse keyframe shapes at specific times. Our approach demonstrates excellent efficiency and we demonstrate this on high resolution meshes with optimizations involving over 26 million degrees of freedom. Making an existing solver differentiable requires only a few modifications and the model is compatible with both modern CPU and GPU multi-core hardware.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Computer Science Applications

Reference43 articles.

1. Large steps in cloth simulation

2. Physics-driven pattern adjustment for direct 3D garment editing

3. Jan Bender, Matthias Müller, and Miles Macklin. 2017. A Survey on Position Based Dynamics, 2017. In EUROGRAPHICS 2017 Tutorials. Eurographics Association.

4. Projective dynamics

5. Capturing detailed deformations of moving human bodies;Chen He;ACM Transactions on Graphics (TOG),2021

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