FaceGID: Facial Mesh Expression Retargeting with Geometry-image Diffusion

3D facial expression retargeting remains challenging due to the irregular structure of mesh data, the scarcity of large-scale 3D facial datasets, and the limitations of relying on fixed templates or reference meshes. In this work, we formulate this task as a 2D conditional generation problem using geometry images as the 3D representation. This allows us to fine-tune a pretrained 2D image generative model on geometry images and leverage priors learned from large-scale 2D data for 3D generation. Given a source facial mesh and a single reference image, our method transfers the reference expression while preserving the source identity and capturing expression-related appearance changes. We further introduce a training-free guidance strategy that enables controllable expression strength at inference time. Experiments demonstrate effective expression retargeting and good generalization across different facial meshes and reference images.