FCMD: Fine-Grained Text-Driven Cohesive Motion Generation With Diffusion Model

Generating continuous and expressive human motion from textual descriptions is a critical challenge in applications such as gaming and filmmaking. Existing methods often struggle to maintain global coherence, realistic frame continuity, and smooth transitions. To address these limitations, we propose FCMD, a novel diffusion-based model for generating cohesive motion sequences from fine-grained textual descriptions. FCMD introduces three key innovations: (1) Fine-grained Text Fusion, which integrates detailed textual cues with transitional narratives to enhance semantic consistency; (2) History Motion Guidance, ensuring motion accuracy and consistency across consecutive frames; and (3) Smooth Stitching Sampling, which leverages preceding and current motion information to achieve seamless transitions. Additionally, FCMD employs a large language model (LLM) to refine motion datasets by extracting fine-grained textual descriptions. Extensive experiments demonstrate that FCMD outperforms state-of-the-art methods in generating coherent, natural, and highly controllable motion sequences.