ExDiff: Diffusion as an Expressive Prior for Hand Gestures and Facial Expressions

Expressive hand gestures and facial expressions provide essential non-verbal cues for understanding human intent in image-based human-computer interac- tion. However, existing pose priors often focus on full-body motion or model the hands and face as independent components, limiting their ability to capture the dependencies between hand articulation and facial expression. We propose EXD- IFF, an expressive diffusion framework for jointly modeling bilateral hand ges- tures, jaw motion, and facial expressions within a unified expressive latent space. The proposed framework introduces part-balanced diffusion with part-aware noise schedules, cross-part residual fusion, and geometry-aware denoising supervision to model fine-grained hand articulation, facial dynamics, and their mutual depen- dencies. To support heterogeneous training, EXDIFF learns part-specific hand and face diffusion priors, then trains a zero-initialized cross-part residual fusion head on joint hand–face data, using part-availability masks and confidence-aware con- ditioning. The learned prior supports expressive generation, missing-part comple- tion, and monocular hand–face mesh recovery. Experiments show that EXDIFF consistently improves realism, coverage, and diversity on ARCTIC, FreiHAND, NOW, and BEAT2. The code will be made available to the public.