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.

翻译：我们提出了一种新颖的3D人脸再老化框架，可实现高精细度、身份保持的真实可控效果。现有3D编辑方法虽能有效处理粗粒度语义变化，但在再老化场景中表现欠佳——即使再老化2D视图间存在微小不一致性，也会导致感知上重要的年龄相关细节过度平滑。针对这一挑战，我们首先引入基于2D扩散的DiffReaging再老化模型，该模型在合成图像对上进行训练。进一步提出中心向外编辑传播策略，利用该再老化模型重建多视图一致的再老化图像。具体而言，从再老化后的正面基准视图出发，通过图像变形及所提出的Masked-DiffReaging过程重建其余视图。通过在扩散过程每一步注入现有内容，Masked-DiffReaging技术确保重建区域与已有像素保持连贯性。最终获得的一致性再老化视图集将监督三维表示的优化过程。我们的方法在视觉效果和量化指标上均超越现有3D编辑技术，实现了对3D人脸模型年龄变换的平滑细粒度控制。