摘要
Recently, 3D face reconstruction from a single image has achieved promising progress by adopting the 3D Morphable Model (3DMM). However, face images taken in-the-wild usually involve expressions with a large range of variety. This poses difficulty to use 3DMM to represent such various facial expressions owing to the limited expressive ability of its linear model, thereby resulting in distortion and ambiguity in local facial regions. To tackle this problem, we present a novel dual-stream network composed of a geometry stream and a texture stream to deal with expression variations. Specifically, in the geometry stream, we propose novel Attribute Spatial Maps (ASMs) to decompose a face into the identity and expression attributes and then separately record the essential spatial information of the two facial attributes in the 2D image space. This avoids the interaction between the two attributes, thus preserving the identity information and further improving the ability of coping with expression variations. In the texture stream, we propose to generate facial appearance with realistic texture and canonical layout by our Semantic Region Stylization Mechanism (SRSM), that transfers the style from an input face to a 3DMM albedo map in a region-adaptive manner. Moreover, we also propose a Shared Semantic Region Prediction Module (SSRPM) to explore the common correspondence of semantic regions between the above two face texture representations. Both quantitative and qualitative evaluations on public datasets demonstrate the effectiveness of our approach in face reconstruction under expression variations.