Self-supervised Learning of Latent Space Dynamics

2025-07-10

AI Summary

To address the efficiency bottleneck of real-time physics simulation for deformable objects on resource-constrained platforms (e.g., VR headsets and mobile devices), this paper proposes a self-supervised dynamical modeling method based on a neural implicit spatial integrator. The approach employs nonlinear dimensionality reduction to construct a low-dimensional implicit latent space, where dynamics evolution is directly predicted—bypassing costly full-space computations and high-frequency detail reconstruction. Crucially, it requires no explicit physical labels or predefined subspaces, enabling end-to-end self-supervised training. Extensive evaluation across diverse deformable object categories—including rods, shells, and volumetric solids—demonstrates that our method achieves real-time frame rates while preserving high visual fidelity and strong cross-scenario generalization. It overcomes longstanding limitations of subspace methods in computational efficiency, numerical stability, and deployment flexibility.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Learning with Manifolds
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Modeling the dynamic behavior of deformable objects is crucial for creating realistic digital worlds. While conventional simulations produce high-quality motions, their computational costs are often prohibitive. Subspace simulation techniques address this challenge by restricting deformations to a lower-dimensional space, improving performance while maintaining visually compelling results. However, even subspace methods struggle to meet the stringent performance demands of portable devices such as virtual reality headsets and mobile platforms. To overcome this limitation, we introduce a novel subspace simulation framework powered by a neural latent-space integrator. Our approach leverages self-supervised learning to enhance inference stability and generalization. By operating entirely within latent space, our method eliminates the need for full-space computations, resulting in a highly efficient method well-suited for deployment on portable devices. We demonstrate the effectiveness of our approach on challenging examples involving rods, shells, and solids, showcasing its versatility and potential for widespread adoption.

Problem

• Model dynamic behavior of deformable objects efficiently
• Reduce computational costs for portable device deployment
• Enhance simulation stability and generalization using self-supervised learning

Innovation

• Neural latent-space integrator for dynamics
• Self-supervised learning enhances stability
• Full-space computation elimination for efficiency