Exploring 3D-aware Lifespan Face Aging via Disentangled Shape-Texture Representations
This addresses face aging for computer vision applications, offering a novel approach but is incremental in improving existing methods.
The paper tackled the problem of face aging by disentangling shape and texture representations using 3D face reconstruction, achieving state-of-the-art performance in shape and texture transformation and producing plausible 3D face aging results.
Existing face aging methods often focus on modeling either texture aging or using an entangled shape-texture representation to achieve face aging. However, shape and texture are two distinct factors that mutually affect the human face aging process. In this paper, we propose 3D-STD, a novel 3D-aware Shape-Texture Disentangled face aging network that explicitly disentangles the facial image into shape and texture representations using 3D face reconstruction. Additionally, to facilitate high-fidelity texture synthesis, we propose a novel texture generation method based on Empirical Mode Decomposition (EMD). Extensive qualitative and quantitative experiments show that our method achieves state-of-the-art performance in terms of shape and texture transformation. Moreover, our method supports producing plausible 3D face aging results, which is rarely accomplished by current methods.