Retargeting of facial model for unordered dense point cloud

Facial retargeting is a widely used technique in the game and film industries that replicates the expressions of a source facial model onto a target model. Existing methods for facial retargeting rely on either hand-crafted uniform triangle meshes or sparse points obtained from motion capture(mocap). In this paper, we propose an end-to-end facial retargeting algorithm that copies facial expressions from unordered dense point clouds onto the target model. First, a corresponding building method based on bi-harmonic function is introduced to ensure that the template model and a cluster of point clouds share the same triangle topology. Second, a deformation transferring method is presented to transfer the calculated deformation onto the target model. Several experiments are conducted on the SIAT-3DFE dataset to demonstrate the accuracy and efficiency of our method. •We developed a method that leverages bi-harmonic functions to accurately warp the template model into a cluster of unordered point clouds.•We put forward a robust deformation transferring approach that effectively transfers the source facial expression onto the target model.•We conduct several quantitative and qualitative experimental results for our proposed non-rigid registration and deformation transferring method.