Differentiable Depth for Real2Sim Calibration of Soft Body Simulations

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In this work, we present a novel approach for calibrating material model parameters for soft body simulations using real data. We use a fully differentiable pipeline, combining a differentiable soft body simulator and differentiable depth rendering, which permits fast gradient-based optimizations. Our method requires no data pre-processing, and minimal experimental set-up, as we directly minimize the L2-norm between raw LIDAR scans and rendered simulation states. In essence, we provide the first marker-free approach for calibrating a soft-body simulator to match observed real-world deformations. Our approach is inexpensive as it solely requires a consumer-level LIDAR sensor compared to acquiring a professional marker-based motion capture system. We investigate the effects of different material parameterizations and evaluate convergence for parameter optimization in both single and multi-material scenarios of varying complexity. Finally, we show that our set-up can be extended to optimize for dynamic behaviour as well.

Original languageEnglish
JournalComputer Graphics Forum
Volume42
Issue number1
Pages (from-to)277-289
Number of pages13
ISSN0167-7055
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Computer Graphics Forum published by Eurographics - The European Association for Computer Graphics and John Wiley & Sons Ltd.

    Research areas

  • animation, methods and applications, physically based animation, ray tracing, rendering, robotics

ID: 339158200