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Single image 3D face reconstruction with accurate geometric details is a critical and challenging task due to the similar appearance on the face surface and fine details in organs. In this work, we introduce a self-supervised 3D face reconstruction approach from a single image that can recover detailed textures under different camera settings. The proposed network learns high-quality disparity maps from stereo face images during the training stage, while just a single face image is required to generate the 3D model in real applications. To recover fine details of each organ and facial surface, the framework introduces facial landmark spatial consistency to constrain the face recovering learning process in local point level and segmentation scheme on facial organs to constrain the correspondences at the organ level. The face shape and textures will further be refined by establishing holistic constraints based on the varying light illumination and shading information. The proposed learning framework can recover more accurate 3D facial details both quantitatively and qualitatively compared with state-of-the-art 3DMM and geometry-based reconstruction algorithms based on a single image.
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Directionally Convolutional Networks for 3D Shape Segmentation
Previous approaches on 3D shape segmentation mostly rely on heuristic processing and hand-tuned geometric descriptors. In this paper, we propose a novel 3D shape representation learning approach, Directionally Convolutional Network (DCN), to solve the shape segmentation problem. DCN extends convolution operations from images to the surface mesh of 3D shapes. With DCN, we learn effective shape representations from raw geometric features, i.e., face normals and distances, to achieve robust segmentation. More specifically, a two-stream segmentation framework is proposed: one stream is made up by the proposed DCN with the face normals as the input, and the other stream is implemented by a neural network with the face distance histogram as the input. The learned shape representations from the two streams are fused by an element-wise product. Finally, Conditional Random Field (CRF) is applied to optimize the segmentation. Through extensive experiments conducted on benchmark datasets, we demonstrate that our approach outperforms the current state-of-the-arts (both classic and deep learning-based) on a large variety of 3D shapes.
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- Award ID(s):
- 1657364
- PAR ID:
- 10058462
- Date Published:
- Journal Name:
- IEEE International Conference on Computer Vision (ICCV)
- Page Range / eLocation ID:
- 2698 - 2707
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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