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arXiv · 2606.18156

ReAge3D: Re-Aging 3D Faces with View Consistency

Libing Zeng·Li Ma·Mingming He·Ning Yu·Paul Debevec·Nima Khademi Kalantari
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Abstract

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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Libing Zeng, Li Ma, Mingming He, Ning Yu, Paul Debevec, Nima Khademi Kalantari. 2026-06-16. ReAge3D: Re-Aging 3D Faces with View Consistency. https://arxiv.org/abs/2606.18156

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