ReAge3D: Re-Aging 3D Faces with View Consistency

Problem — The paper addresses the limitations of existing 3D editing methods in the context of face re-aging, particularly the inability to maintain consistency across multiple 2D views. Current techniques often lead to over-smoothing of age-related details, which diminishes the realism and identity preservation in the re-aged outputs. This work is a preprint and has not undergone peer review.

Method — The authors propose a two-part framework for 3D face re-aging. The first component is a 2D diffusion-based model called DiffReaging, which is trained on synthetically generated image pairs to facilitate the re-aging process. The second component is a center-out editing propagation strategy that utilizes DiffReaging to ensure multi-view consistency. Starting from a re-aged frontal view, the method reconstructs additional views through a warping technique combined with a novel Masked-DiffReaging process. This process injects existing pixel content at each step of the diffusion, ensuring coherence in the reconstructed regions. The optimization of the 3D representation is guided by the consistent set of re-aged images generated.

Results — The proposed ReAge3D framework demonstrates superior performance compared to existing 3D editing techniques. The authors report significant improvements in both visual quality and quantitative metrics, although specific numerical results and baseline comparisons are not detailed in the abstract. The method allows for fine-grained control over age transformations, which is a notable advancement in the field of 3D face modeling.

Limitations — The authors acknowledge that their approach relies on synthetic data for training, which may limit the generalizability of the model to real-world scenarios. Additionally, the paper does not discuss the computational requirements for training or inference, which could be a concern for practical applications. The reliance on a frontal pivot view may also introduce biases in the re-aging process, as variations in head pose could affect the quality of the reconstructed views.

Why it matters — The implications of this work extend to various applications in computer graphics, virtual reality, and digital content creation, where realistic and controllable face re-aging is essential. The ability to maintain view consistency while preserving identity opens new avenues for character animation and personalized digital avatars. This research contributes to the growing body of work in 3D face manipulation and highlights the potential of diffusion models in achieving high-fidelity results, as published in arXiv cs.AI.