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CGOF++: Controllable 3D Face Synthesis With Conditional Generative Occupancy Fields

  • Published:2024-02 Issue:2 Volume:46 Page:913-926
  • ISSN:0162-8828
  • Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
  • language:
  • Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Sun Keqiang1ORCID,Wu Shangzhe2ORCID,Zhang Ning3ORCID,Huang Zhaoyang1ORCID,Wang Quan3ORCID,Li Hongsheng4ORCID

Affiliation:

1. Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong SAR

2. Department of Engineering Science, University of Oxford, Oxford, U.K.

3. SenseTime Research, Beijing, China

4. CUHK MMlab, Centre for Perceptual and Interactive Intelligence, Shanghai AI Laboratory, Shanghai, China

Funder

National Key R&D Program of China

Centre for Perceptual and Interactive Intelligence

General Research Fund of Hong Kong RGC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Reference79 articles.

1. Efficient Geometry-aware 3D Generative Adversarial Networks

2. Generative adversarial nets;Goodfellow

3. Large scale GAN training for high fidelity natural image synthesis;Brock

4. A Style-Based Generator Architecture for Generative Adversarial Networks

5. Analyzing and Improving the Image Quality of StyleGAN

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. CGOF++: Controllable 3D Face Synthesis With Conditional Generative Occupancy Fields;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-02
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