实现3D人脸自然重龄，多视角保持一致性。
ReAge3D: Re-Aging 3D Faces with View Consistency
- 基于扩散模型生成重龄图像，再通过渐进式修复确保多视角一致
- 以正面视图为起点，逐次重建其他视角，保留细微年龄特征
- 适合需要精细控制人脸衰老过程的影视与虚拟形象应用
我们提出一种新颖的3D人脸重龄框架，可生成高细节、身份一致的逼真结果。现有3D编辑方法虽能处理粗粒度语义变化，但在重龄任务中表现不佳，因重龄后2D视角间的小不一致会导致细微但重要的年龄特征被过度平滑。为此，我们首先训练了一个基于扩散的重龄模型DiffReaging，使用合成图像对进行训练。进一步提出中心向外编辑传播策略：以重龄后的正面视图为起点，通过图像扭曲和提出的Masked-DiffReaging过程重建其余视角。在每一步扩散过程中注入已有内容，确保重建区域与已有像素保持一致。由此生成的一致性重龄视图监督3D表示的优化。实验表明，该方法在视觉与定量评价上均优于现有3D编辑技术，实现对3D人脸衰老过程的平滑、细粒度控制。
原文摘要 · Abstract (English)
We present a novel framework for realistic and controllable 3D face re-aging which produces highly detailed, identity-preserving results. Existing 3D editing methods, while effective for coarse semantic changes, are not well suited for re-aging, as even small inconsistencies across re-aged 2D views can lead to over-smoothing of subtle but perceptually important age-related details. To address this challenge, we first introduce a 2D diffusion-based re-aging model, DiffReaging, trained on synthetically generated image pairs. We further propose a center-out editing propagation strategy that leverages this re-aging model to reconstruct multi-view-consistent re-aged images. Specifically, starting from a re-aged frontal pivot view, we reconstruct the remaining views through warping and our proposed Masked-DiffReaging process. By injecting existing content at every step of the diffusion process, Masked-DiffReaging ensures that the reconstructed regions remain coherent with existing pixels. The resulting consistent set of re-aged views supervises the optimization of the re-aged 3D representation. Our method outperforms existing 3D editing techniques both visually and quantitatively, enabling smooth, fine-grained control over age transformations in 3D face models.
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