Recent years have witnessed significant progress in image-based 3D face reconstruction using deep convolutional neural networks.However,current reconstruction methods often perform improperly in self-occluded regions ...Recent years have witnessed significant progress in image-based 3D face reconstruction using deep convolutional neural networks.However,current reconstruction methods often perform improperly in self-occluded regions and can lead to inaccurate correspondences between a 2D input image and a 3D face template,hindering use in real applications.To address these problems,we propose a deep shape reconstruction and texture completion network,SRTC-Net,which jointly reconstructs 3D facial geometry and completes texture with correspondences from a single input face image.In SRTC-Net,we leverage the geometric cues from completed 3D texture to reconstruct detailed structures of 3D shapes.The SRTC-Net pipeline has three stages.The first introduces a correspondence network to identify pixel-wise correspondence between the input 2D image and a 3D template model,and transfers the input 2D image to a U-V texture map.Then we complete the invisible and occluded areas in the U-V texture map using an inpainting network.To get the 3D facial geometries,we predict coarse shape(U-V position maps)from the segmented face from the correspondence network using a shape network,and then refine the 3D coarse shape by regressing the U-V displacement map from the completed U-V texture map in a pixel-to-pixel way.We examine our methods on 3D reconstruction tasks as well as face frontalization and pose invariant face recognition tasks,using both in-the-lab datasets(MICC,MultiPIE)and in-the-wild datasets(CFP).The qualitative and quantitative results demonstrate the effectiveness of our methods on inferring 3D facial geometry and complete texture;they outperform or are comparable to the state-of-the-art.展开更多
基金supported by the National Natural Science Foundation of China(Nos.U1613211 and U1813218)Shenzhen Research Program(Nos.JCYJ20170818164704758 and JCYJ20150925163005055).
文摘Recent years have witnessed significant progress in image-based 3D face reconstruction using deep convolutional neural networks.However,current reconstruction methods often perform improperly in self-occluded regions and can lead to inaccurate correspondences between a 2D input image and a 3D face template,hindering use in real applications.To address these problems,we propose a deep shape reconstruction and texture completion network,SRTC-Net,which jointly reconstructs 3D facial geometry and completes texture with correspondences from a single input face image.In SRTC-Net,we leverage the geometric cues from completed 3D texture to reconstruct detailed structures of 3D shapes.The SRTC-Net pipeline has three stages.The first introduces a correspondence network to identify pixel-wise correspondence between the input 2D image and a 3D template model,and transfers the input 2D image to a U-V texture map.Then we complete the invisible and occluded areas in the U-V texture map using an inpainting network.To get the 3D facial geometries,we predict coarse shape(U-V position maps)from the segmented face from the correspondence network using a shape network,and then refine the 3D coarse shape by regressing the U-V displacement map from the completed U-V texture map in a pixel-to-pixel way.We examine our methods on 3D reconstruction tasks as well as face frontalization and pose invariant face recognition tasks,using both in-the-lab datasets(MICC,MultiPIE)and in-the-wild datasets(CFP).The qualitative and quantitative results demonstrate the effectiveness of our methods on inferring 3D facial geometry and complete texture;they outperform or are comparable to the state-of-the-art.
