Continuous-time neural solver improves cloth simulation accuracy and speed
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs
Machine LearningRobotics
Summary
Simulating how cloth moves and bends can be slow and only works at certain times. The authors created ContinuousCloth, a new method that predicts cloth movement continuously over time using physics rules inside a neural network. This lets users check cloth positions at any moment and get speeds and accelerations easily. It is both faster and more accurate than traditional methods and helps fix errors when data is noisy or incomplete.
What this means in practice
- •For animation and visual effects teams: Simulate realistic cloth movement in films and games more accurately and efficiently with fewer computational resources.
- •For robotics engineers: Estimate cloth material properties and predict cloth behavior under manipulation using sparse sensor data for better robotic cloth handling.
Authors
Yingdong Ru, Lipeng Zhuang, Zhuo He, Gerardo Aragon Camarasa
Abstract
We present ContinuousCloth, a continuous-time, physics-informed neural solver for deformable cloth dynamics that enforces a spring--mass ODE within a neural trajectory model. Unlike classical time-stepping simulators that return trajectories only on a discrete integration grid, ContinuousCloth supports querying at arbitrary timestamps and computing displacement, velocity, and acceleration via automatic differentiation. We evaluate ContinuousCloth along two dimensions. In a physics-only setting, ContinuousCloth achieves trajectory accuracy comparable to high-precision solvers across six diverse boundary-condition scenarios while reducing runtime by up to 17.4 times. When fused with sparse and noisy observations, the spring--mass ODE acts as a strong inductive bias for downstream tasks: interpolation and parameter estimation. That is, ContinuousCloth reduces interpolation error by 7.3 times over a data-only neural baseline in simulation and 2.4 times on real-world cloth sequences, and achieves 2.6 times lower parameter error than a differentiable-solver baseline under high-noise conditions, demonstrating consistent robustness under realistic measurement imperfections.