MorphoStyle: Motion Style Transfer with Morphology Control

2026-09-12

AI Summary

本文提出MorphoStyle框架，通过形状条件的FSQ-VAE实现非标准体型的动作风格迁移，解决了现有方法中动作内容、风格和形态分离的问题。

Abstract

Human motion may be viewed as a combination of action content, style, and body morphology. Existing motion style transfer methods transfer a reference style onto a content motion while assuming a canonical body, whereas shape-aware motion generators adapt motion to a target shape without explicit style control. This separation of motion style and shape (morphology) makes it difficult to generate stylized motions for non-canonical bodies; naively combining a style transfer module with a shape-aware generator often leaks action content from the style reference and disrupts shape-consistent kinematics. We present MorphoStyle, a framework for shape-aware motion style transfer that is built on a shape-conditioned Finite-Scalar-Quantization Variational Auto-Encoder (FSQ-VAE). The key contribution is to pose the desired style transfer as modular latent disentanglement comprising: (i) a contrastive style encoder that extracts content-decoupled style embeddings; (ii) a text-guided style-routing mechanism that locates style-relevant joints in a text-motion feature space; and (iii) a manifold preserving style modulator that injects discriminative style embeddings in content features as a temporally-gated low-rank offset. Extensive experiments on benchmark datasets demonstrate that MorphoStyle outperforms competing baselines in terms of both shape control and motion style transfer, while simultaneously providing quantitative shape control. For more details, please see project website: https://github.com/funkdub/MorphoStyle.

Problem

• motion style transfer
• body morphology
• shape-aware motion generation

Innovation

• shape-aware motion style transfer
• contrastive style encoder
• text-guided style routing
• manifold preserving style modulator
• Finite-Scalar-Quantization Variational Auto-Encoder (FSQ-VAE)