Human Motion Generation

Human motion generation studies how to synthesize or edit body movement for animation, avatars, interaction, and humanoid robots. Common approaches use diffusion or flow-matching models, autoregressive and masked motion tokens, and structured representations of body parts or joint trajectories; retrieval, planning, and physics-based refinement can add control. Text-to-motion is often tested on HumanML3D and KIT-ML, while dance and speech-driven gestures use benchmarks such as AIST++ and BEAT. A central challenge is balancing semantic accuracy, natural and diverse motion, physical plausibility, and precise control—especially for long or real-time sequences, where evaluation metrics may not reflect human judgments or successful execution.