Semantic 3D-Aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field | |
Tianxiang Ma3,4; Bingchuan Li1; Qian He1; Jing Dong3; Tieniu Tan2,3 | |
2023 | |
会议日期 | 2.7-2.14 |
会议地点 | 美国华盛顿 |
英文摘要 | Recently 3D-aware GAN methods with neural radiance field have developed rapidly. However, current methods model the whole image as an overall neural radiance field, which limits the partial semantic editability of synthetic results. Since NeRF renders an image pixel by pixel, it is possible to split NeRF in the spatial dimension. We propose a Compositional Neural Radiance Field (CNeRF) for semantic 3D-aware portrait synthesis and manipulation. CNeRF divides the image by semantic regions and learns an independent neural radiance field for each region, and finally fuses them and renders the complete image. Thus we can manipulate the synthesized semantic regions independently, while fixing the other parts unchanged. Furthermore, CNeRF is also designed to decouple shape and texture within each semantic region. Compared to state-of-the-art 3D-aware GAN methods, our approach enables fine-grained semantic region manipulation, while maintaining high-quality 3D-consistent synthesis. The ablation studies show the effectiveness of the structure and loss function used by our method. In addition real image inversion and cartoon portrait 3D editing experiments demonstrate the application potential of our method. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/56661] |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Jing Dong |
作者单位 | 1.ByteDance Ltd, Beijing, China 2.Nanjing University 3.CRIPAC & NLPR, Institute of Automation, Chinese Academy of Sciences 4.School of Artificial Intelligence, University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Tianxiang Ma,Bingchuan Li,Qian He,et al. Semantic 3D-Aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field[C]. 见:. 美国华盛顿. 2.7-2.14. |
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