  Text-to-image generative models can generate high-quality humans, but realism
is lost when generating hands. Common artifacts include irregular hand poses,
shapes, incorrect numbers of fingers, and physically implausible finger
orientations. To generate images with realistic hands, we propose a novel
diffusion-based architecture called HanDiffuser that achieves realism by
injecting hand embeddings in the generative process. HanDiffuser consists of
two components: a Text-to-Hand-Params diffusion model to generate SMPL-Body and
MANO-Hand parameters from input text prompts, and a Text-Guided
Hand-Params-to-Image diffusion model to synthesize images by conditioning on
the prompts and hand parameters generated by the previous component. We
incorporate multiple aspects of hand representation, including 3D shapes and
joint-level finger positions, orientations and articulations, for robust
learning and reliable performance during inference. We conduct extensive
quantitative and qualitative experiments and perform user studies to
demonstrate the efficacy of our method in generating images with high-quality
hands.
