Differentiable XPBD for Gradient-Based Learning of Physical Parameters from Motion
| dc.contributor.author | Drysch, Simone | en_US |
| dc.contributor.author | Stotko, David | en_US |
| dc.contributor.author | Klein, Reinhard | en_US |
| dc.contributor.editor | Egger, Bernhard | en_US |
| dc.contributor.editor | Günther, Tobias | en_US |
| dc.date.accessioned | 2025-09-24T10:38:26Z | |
| dc.date.available | 2025-09-24T10:38:26Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Accurate cloth simulation is a vital component in computer graphics, virtual reality, and fashion design. Position-Based Dynamics (PBD) and its extension (XPBD) offer robust and efficient methods for simulating deformable objects like cloth. This paper details the evaluation and comparison of cloth simulations based on XPBD, including its ''small steps'' variant and an Energy- Aware (EA) modification. The XPBD variants are evaluated for their physical plausibility and energy conservation to analyze their suitability for inverse problems. Furthermore, we explore the implementation of a differentiable XPBD simulator, enabling the estimation of material properties and external forces. The differentiable simulator is assessed for its capability to estimate parameters in scenarios of increasing complexity. Results indicate that small time steps with single iterations in XPBD offer good energy behavior, while the EA modification exhibits undesired characteristics. The differentiable simulator successfully estimates single parameters but identifies challenges with multi-parameter optimization due to compensatory effects. | en_US |
| dc.description.sectionheaders | Geometry, Simulation, and Optimization | |
| dc.description.seriesinformation | Vision, Modeling, and Visualization | |
| dc.identifier.doi | 10.2312/vmv.20251244 | |
| dc.identifier.isbn | 978-3-03868-294-3 | |
| dc.identifier.pages | 8 pages | |
| dc.identifier.uri | https://doi.org/10.2312/vmv.20251244 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/vmv20251244 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing Methodologies → Modeling and simulation; Applied computing → Physical sciences and engineering | |
| dc.subject | Computing Methodologies → Modeling and simulation | |
| dc.subject | Applied computing → Physical sciences and engineering | |
| dc.title | Differentiable XPBD for Gradient-Based Learning of Physical Parameters from Motion | en_US |