Designing a 3D-Aware StyleNeRF Encoder for Face Editing | |
Yang, Songlin![]() ![]() | |
2023-04 | |
会议名称 | IEEE International Conference on Acoustics, Speech, and Signal Processing |
会议日期 | 2023-6-8 |
会议地点 | Greek island of Rhodes |
摘要 | GAN inversion has been exploited in many face manipulation tasks, but 2D GANs often fail to generate multi-view 3D consistent images. The encoders designed for 2D GANs are not able to provide sufficient 3D information for the inversion and editing. Therefore, 3D-aware GAN inversion is proposed to increase the 3D editing capability of GANs. However, the 3D-aware GAN inversion remains under-explored. To tackle this problem, we propose a 3D-aware (3Da) encoder for GAN inversion and face editing based on the powerful StyleNeRF model. Our proposed 3Da encoder combines a parametric 3D face model with a learnable detail representation model to generate geometry, texture and view direction codes. For more flexible face manipulation, we then design a dual-branch StyleFlow module to transfer the StyleNeRF codes with disentangled geometry and texture flows. Extensive experiments demonstrate that we realize 3D consistent face manipulation in both facial attribute editing and texture transfer. Furthermore, for video editing, we make the sequence of frame codes share a common canonical manifold, which improves the temporal consistency of the edited attributes. |
收录类别 | EI |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57548 |
专题 | 模式识别实验室 |
通讯作者 | Wang, Wei |
作者单位 | Institute of Automation, Chinese Academy of Sciences(CASIA) |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yang, Songlin,Wang, Wei,Peng, Bo,et al. Designing a 3D-Aware StyleNeRF Encoder for Face Editing[C],2023. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ICASSP_2023__StyleNe(4486KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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