统一控制人脸外观、动作和光照，实现更逼真的肖像编辑。
Total-Editing: Head Avatar with Editable Appearance, Motion, and Lighting
- 用神经辐射场分解内在属性，融合光照信息生成新肖像。
- 通过最小二乘变形场提升动作与光影的时空一致性。
- 支持跨肖像光照迁移和自定义背景动画，应用灵活。
人脸重演和肖像补光是肖像编辑的关键任务，但通常独立处理，缺乏协同。现有方法分别侧重运动控制与多视角一致性，或调整明暗效果。为同时利用几何一致性和光照感知能力，我们提出Total-Editing，一个统一的肖像编辑框架，可精确调控外观、运动与光照。具体地，设计具备内在分解能力的神经辐射场解码器，将肖像图像或HDR环境图的光照信息无缝融入合成肖像。同时引入基于最小二乘的变形场，增强角色动作与阴影的时空连贯性。该框架显著提升肖像编辑的质量与真实感。此外，其多源特性支持更灵活的应用，如从一张肖像迁移到另一张的光照转移，或在自定义背景上进行人物动画。
原文摘要 · Abstract (English)
Face reenactment and portrait relighting are essential tasks in portrait editing, yet they are typically addressed independently, without much synergy. Most face reenactment methods prioritize motion control and multiview consistency, while portrait relighting focuses on adjusting shading effects. To take advantage of both geometric consistency and illumination awareness, we introduce Total-Editing, a unified portrait editing framework that enables precise control over appearance, motion, and lighting. Specifically, we design a neural radiance field decoder with intrinsic decomposition capabilities. This allows seamless integration of lighting information from portrait images or HDR environment maps into synthesized portraits. We also incorporate a moving least squares based deformation field to enhance the spatiotemporal coherence of avatar motion and shading effects. With these innovations, our unified framework significantly improves the quality and realism of portrait editing results. Further, the multi-source nature of Total-Editing supports more flexible applications, such as illumination transfer from one portrait to another, or portrait animation with customized backgrounds.
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