FG-Portrait：三维流引导的可编辑肖像动画
FG-Portrait: 3D Flow Guided Editable Portrait Animation
把参数化三维人脸模型算出的稠密三维流当作几何先验注入扩散模型，用三维流编码为每个目标像素查询位移，并用深度引导采样对齐二维运动；这套「三维几何先验 + 扩散条件控制」思路可迁移到任何需要源到驱动对应关系的可控人像/人体生成任务。
原文依据 we introduce 3D flow encoding to query potential 3D flows for each target pixel to indicate its displacement back to the source location
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论将驱动肖像的运动迁移到源肖像上，仍是肖像动画中的一项关键挑战。当前基于扩散模型的方法仅以驱动运动为条件，无法捕捉源肖像与驱动肖像之间的对应关系，因而运动迁移效果欠佳。尽管光流估计提供了另一种途径，但从二维输入预测稠密对应关系本身是病态问题，往往产生不准确的动画。我们通过引入三维流来解决这一问题：三维流是一种无需学习、由几何驱动的运动对应关系，直接由参数化三维头部模型计算得到。为将该三维先验融入扩散模型，我们提出三维流编码，为每个目标像素查询潜在的三维流，以指示其回到源位置的位移。为获得与二维运动变化对齐的三维流，我们进一步提出深度引导采样，以准确为每个像素定位对应的三维点。除高保真肖像动画外，我们的模型还支持用户对表情和头部姿态进行指定编辑。大量实验表明，本方法在一致的驱动运动迁移以及忠实的源身份保持方面均具有优越性。
英文原文摘要 1217 字符
Motion transfer from the driving to the source portrait remains a key challenge in the portrait animation. Current diffusion-based approaches condition only on the driving motion, which fails to capture source-to-driving correspondences and consequently yields suboptimal motion transfer. Although flow estimation provides an alternative, predicting dense correspondences from 2D input is ill-posed and often yields inaccurate animation. We address this problem by introducing 3D flows, a learning-free and geometry-driven motion correspondence directly computed from parametric 3D head models. To integrate this 3D prior into diffusion model, we introduce 3D flow encoding to query potential 3D flows for each target pixel to indicate its displacement back to the source location. To obtain 3D flows aligned with 2D motion changes, we further propose depth-guided sampling to accurately locate the corresponding 3D points for each pixel. Beyond high-fidelity portrait animation, our model further supports user-specified editing of facial expression and head pose. Extensive experiments demonstrate the superiority of our method on consistent driving motion transfer as well as faithful source identity preservation.