Hi-Motion: Hierarchical Intention Guided Conditional Motion Synthesis

Text-conditioned motion generation has significant applications across various domains. However, generating natural motion remains challenging due to the vast solution space and the accumulation of errors during motion generation. To address these challenges, a novel hierarchical motion intention decoding-based motion synthesis model named Hi-Motion is proposed, which disentangles human motion into temporal intents of pivot joints and skeleton synthesis guided by intention from a new perspective. Specifically, Hi-Motion first parameterizes pivot joint motion with high-order Bézier curves and constructs a Bézier decoder to generate their trajectories, which serve as motion intention to guide skeleton generation. Secondly, we formulate the generation of skeletons as a graph node transformation problem under the condition of determined edge connections. By incorporating hierarchical joint motion intentions into the graph node features, the spatial details of each frame can be precisely synthesized. The proposed Hi-Motion effectively decouples motion generation into temporal and spatial dimensions through hierarchical motion intention decoding, ensuring coordination and naturalness in the generated motion. Extensive experiments on HumanML3D and KIT-ML datasets substantiate the motion generation capabilities of Hi-Motion. Further analysis demonstrates that Hi-Motion can accurately predict the motion intention of pivot joints and synthesize skeletal details.