SemFaceEdit：生成辐射流形上的语义人脸编辑
SemFaceEdit: Semantic Face Editing on Generative Radiance Manifolds
AI总结：
本文提出 SemFaceEdit 方法，通过在生成辐射流形上生成语义场，利用潜在编码解开面部几何与外观，实现了精确的局部语义人脸编辑，同时保持其他区域完整性。
摘要：
尽管 3D 感知 GAN 技术提供了多视角一致性，但生成的图像通常缺乏局部编辑的能力。作为回应，生成辐射流形作为一种在体积内进行约束点采样的高效方法出现，有效降低了计算需求并实现了对精细细节的学习。本工作提出了 SemFaceEdit，这是一种通过在生成辐射流形上生成语义场来简化外观与几何编辑过程的新方法。利用潜在编码，我们的方法有效地解开了生成图像中与不同面部语义相关的几何和外观。与只能改变整个辐射场外观的现有方法不同，我们的方法能够精确编辑特定的面部语义，同时保持其他区域的完整性。我们的网络包含两个关键模块：Geometry 模块负责生成语义辐射和占用场，Appearance 模块负责预测 RGB 辐射。我们在对抗设置下联合训练这两个模块，以学习语义感知的几何和外观描述符。随后，Appearance 模块将这些外观描述符以其各自的语义潜在编码为条件，促进解耦并增强控制能力。我们的实验突出了 SemFaceEdit 在基于语义场编辑方面的卓越性能，特别是在实现改进的辐射场解耦方面。
英文摘要：
Despite multiple view consistency offered by 3D-aware GAN techniques, the resulting images often lack the capacity for localized editing. In response, generative radiance manifolds emerge as an efficient approach for constrained point sampling within volumes, effectively reducing computational demands and enabling the learning of fine details. This work introduces SemFaceEdit, a novel method that streamlines the appearance and geometric editing process by generating semantic fields on generative radiance manifolds. Utilizing latent codes, our method effectively disentangles the geometry and appearance associated with different facial semantics within the generated image. In contrast to existing methods that can change the appearance of the entire radiance field, our method enables the precise editing of particular facial semantics while preserving the integrity of other regions. Our network comprises two key modules: the Geometry module, which generates semantic radiance and occupancy fields, and the Appearance module, which is responsible for predicting RGB radiance. We jointly train both modules in adversarial settings to learn semantic-aware geometry and appearance descriptors. The appearance descriptors are then conditioned on their respective semantic latent codes by the Appearance Module, facilitating disentanglement and enhanced control. Our experiments highlight SemFaceEdit's superior performance in semantic field-based editing, particularly in achieving improved radiance field disentanglement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
