ReAge3D: Re-Aging 3D Faces with View Consistency
The 37th Eurographics Symposium on Rendering (EGSR 2026)

Teaser

Given a 3D face model, represented here using 3D Gaussian splatting, our proposed method enables precise age manipulation with fine-grained detail while maintaining multiview consistency and preserving identity. It exhibits smooth and continuous transitions across age ranges from 20 to 80 years, effectively capturing age-related changes such as the gradual formation and deepening of wrinkles. Additionally, the method is robust across a range of facial expressions (e.g. neutral, pucker, and smiles) and generalizes across different identities.

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.

Overview

Overview of Our 3D Face Re-Aging Framework. We first train a diffusion-based re-aging model, DiffReaging, on a synthetic dataset. To extend its re-aging capability to 3D, we introduce a multi-view consistent editing framework. Starting with the pivot view, we re-age it using DiffReaging and warp the results to other views. The Masked-DiffReaging model, adapted from DiffReaging with additional mask constraints, is then used to reconstruct occluded regions. Using a center-out re-aging strategy, we progressively re-age surrounding views. Finally, all re-aged images are used to update the 3D model.

Results

@article{Zeng_2026_reaging,
    author = {Zeng, Libing and Ma, Li and He, Mingming and Yu, Ning and Debevec, Paul and Kalantari, Nima Khademi},
    title = {ReAge3D: Re-Aging 3D Faces with View Consistency},
    booktitle={The 37th Eurographics Symposium on Rendering},
    year={2026}
}