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Graphics Capsule: Learning Hierarchical 3D Face Representations from 2D Images
Yu C(于畅)1,2; Zhu XY(朱翔昱)1,2; Zhang XM(张小梅)1,2; Zhang ZX(张兆翔)1,2,3; Lei Z(雷震)1,2,3
Conference NameIEEE Conference on Computer Vision and Pattern Recognition
Conference Date2023年6月
Conference Place加拿大

The function of constructing the hierarchy of objects is important to the visual process of the human brain. Previous studies have successfully adopted capsule networks to decompose the digits and faces into parts in an unsupervised manner to investigate the similar perception mechanism of neural networks. However, their descriptions are restricted to the 2D space, limiting their capacities to imitate the intrinsic 3D perception ability of humans. In this paper, we propose an Inverse Graphics Capsule Network (IGC-Net) to learn the hierarchical 3D face representations from large-scale unlabeled images. The core of IGC-Net is a new type of capsule, named graphics capsule, which represents 3D primitives with interpretable parameters in computer graphics (CG), including depth, albedo, and 3D pose. Specifically, IGC-Net first decomposes the objects into a set of semantic-consistent part-level descriptions and then assembles them into object-level descriptions to build the hierarchy. The learned graphics capsules reveal how the neural networks, oriented at visual perception, understand faces as a hierarchy of 3D models. Besides, the discovered parts can be deployed to the unsupervised face segmentation task to evaluate the semantic consistency of our method. Moreover, the part-level descriptions with explicit physical meanings provide insight into the face analysis that originally runs in a black box, such as the importance of shape and texture for face recognition. Experiments on CelebA, BP4D, and Multi-PIE demonstrate the characteristics of our IGC-Net.

Sub direction classification生物特征识别
planning direction of the national heavy laboratory可解释人工智能
Paper associated data
Document Type会议论文
Corresponding AuthorZhu XY(朱翔昱)
3.Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yu C,Zhu XY,Zhang XM,et al. Graphics Capsule: Learning Hierarchical 3D Face Representations from 2D Images[C],2023.
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