3D facial feature and expression computing from Internet image or video

Large-scale multimedia datasets such as the Internet image and video collections provide new opportunities to understand and analyze human actions, among which one of the most interesting type is facial performance. In this paper, we present an automatic reconstruction system of detailed face performances. Many existing facial performance reconstruction systems rely on data captured under controlled environments with densely spaced cameras and lights. On the contrary, our system reconstructs detailed facial geometry from just one image or a monocular video sequence with unknown lighting. To achieve this, we first simultaneously track 2D and 3D sparse features, then reconstruct the low frequency facial geometry by performing a 2D-3D feature trajectory fusion optimization, which we formulate as a linear problem that can be solved efficiently. Finally, we use a per-pixel shape-from-shading algorithm to estimate the fine-scale geometry details such as wrinkles to further improve the reconstruction fidelity. We demonstrate the accuracy of our system with reconstruction results using both single images and monocular video sequences.