Odoriko: A Shape-Aware Multimodal Diffusion Framework for Human Motion
This work addresses the overlooked problem of generating motion that reflects individual morphological differences, benefiting applications in animation, virtual reality, and human-robot interaction where realistic, personalized motion is critical.
Odoriko introduces the first unified multimodal motion generation framework that incorporates subject bio-morphological information (e.g., gender, body shape) into synthesized motion across text, music, and video inputs. It matches or exceeds prior specialized models on standard metrics while enabling morphology-consistent generation.
Human motion generation has been widely studied across diverse input modalities, text, music, and video, and recent efforts have unified these into single multimodal frameworks. However, while morphological factors such as gender and body shape are known to produce distinct kinematic signatures, no existing unified framework incorporates this into generation, treating all subjects as morphologically equivalent. We present Odoriko, the first unified multimodal motion generation framework that reflects subject bio-morphological information directly in synthesized motion output. Rather than averaging over subject variation, Odoriko generates motion that is consistent with who is moving, not just what they are asked to do, across text, music, and video conditions within a single model. When explicit morphological information is unavailable, Odoriko additionally recovers subject morphology alongside motion, unifying estimation and generation in one framework. Extensive experiments across text-to-motion, music-to-dance, and video-to-motion benchmarks demonstrate that Odoriko matches or exceeds prior specialized models on standard metrics, while enabling morphology-consistent generation that no existing unified framework supports.