MCSMoG: Multi-Conditional Diffusion for Stylized Motion Generation with Parametric Control

Le résumé fourni par la source

Stylized human motion synthesis remains a fundamental challenge in computer animation and graphics, with a wide spectrum of applications spanning gaming, film production, virtual reality, and beyond. While recent advances in text-driven motion generation have shown promise, existing approaches face critical limitations including the inability to maintain consistent trajectory control, the lack of fine-grained stylization intensity adjustment, and inadequate generalization across diverse motion styles. To address these challenges, We introduce MCSMoG, a novel framework for controllable stylized motion synthesis through multi-conditional guidance. First, a new Multi-Conditional Motion Latent Diffusion (MC-MLD) model is proposed to introduce additional trajectory guidance and achieve trajectory decoupling. Second, we develop a Style and Non-Style Feature Fusion Module that dynamically blends motion features through an adjustable parameter, providing control over stylization intensity. Third, we integrate MotionCLIP as our style encoder, enhancing the model’s generalization capability across diverse and unseen motion styles. Extensive experiments conducted on the combined HumanML3D and 100STYLE datasets demonstrate that our approach outperforms state-of-the-art methods, achieving a 4.6% reduction in FID scores and a 4.1% increase in motion diversity. User studies further confirm the superiority of our method in style fidelity, semantic consistency, and motion naturalness.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.