Generative Face Parsing Map Guided 3D Face Reconstruction Under Occluded Scenes
This addresses the problem of occluded face reconstruction for computer vision applications, representing an incremental improvement over existing unobstructed methods.
The paper tackles 3D face reconstruction from single-view images under occlusion by generating face parsing maps guided by landmarks to estimate reasonable facial structure in occluded areas, demonstrating superior quality and robustness compared to existing methods.
Over the past few years, single-view 3D face reconstruction methods can produce beautiful 3D models. Nevertheless,the input of these works is unobstructed faces.We describe a system designed to reconstruct convincing face texture in the case of occlusion.Motivated by parsing facial features,we propose a complete face parsing map generation method guided by landmarks.We estimate the 2D face structure of the reasonable position of the occlusion area,which is used for the construction of 3D texture.An excellent anti-occlusion face reconstruction method should ensure the authenticity of the output,including the topological structure between the eyes,nose, and mouth. We extensively tested our method and its components, qualitatively demonstrating the rationality of our estimated facial structure. We conduct extensive experiments on general 3D face reconstruction tasks as concrete examples to demonstrate the method's superior regulation ability over existing methods often break down.We further provide numerous quantitative examples showing that our method advances both the quality and the robustness of 3D face reconstruction under occlusion scenes.