Cross-Modal Diffusion for Biomechanical Dynamical Systems Through Local Manifold Alignment

2025-03-15

AI Summary

In cross-modal biomechanical motion generation, aligning complementary signals—such as joint angles and ground reaction forces (GRFs)—remains challenging due to modality mismatch and low generation fidelity. To address this, we propose Local Latent Manifold Alignment (LLMA), a geometry-aware strategy integrated into the denoising process of diffusion models. LLMA jointly models multimodal latent representations within local temporal windows and enforces first-order (tangential) and second-order (curvature) geometric constraints to achieve robust dynamical consistency without auxiliary alignment modules. The framework preserves physical interpretability while enhancing generative quality. Evaluated on standard human biomechanical datasets, LLMA significantly improves cross-modal generation fidelity, yielding joint representations that better adhere to biomechanical principles and exhibit stronger disentanglement.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Diffusion Models for Vision

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

We present a mutually aligned diffusion framework for cross-modal biomechanical motion generation, guided by a dynamical systems perspective. By treating each modality, e.g., observed joint angles ($X$) and ground reaction forces ($Y$), as complementary observations of a shared underlying locomotor dynamical system, our method aligns latent representations at each diffusion step, so that one modality can help denoise and disambiguate the other. Our alignment approach is motivated by the fact that local time windows of $X$ and $Y$ represent the same phase of an underlying dynamical system, thereby benefiting from a shared latent manifold. We introduce a simple local latent manifold alignment (LLMA) strategy that incorporates first-order and second-order alignment within the latent space for robust cross-modal biomechanical generation without bells and whistles. Through experiments on multimodal human biomechanics data, we show that aligning local latent dynamics across modalities improves generation fidelity and yields better representations.

Problem

• Aligns latent representations for cross-modal biomechanical motion generation.
• Uses local manifold alignment to improve generation fidelity.
• Integrates first and second-order alignment for robust biomechanical data synthesis.

Innovation

• Cross-modal diffusion for biomechanical motion generation
• Local latent manifold alignment (LLMA) strategy
• Shared latent manifold for denoising and disambiguation