Exploring 3D-aware Lifespan Face Aging via Disentangled Shape-Texture Representations
원문 초록
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.
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Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction
3D Face Reconstruction Error Decomposed: A Modular Benchmark for Fair and Fast Method Evaluation
3D-GANTex: 3D Face Reconstruction with StyleGAN3-based Multi-View Images and 3DDFA based Mesh Generation
3D Face Reconstruction Using A Spectral-Based Graph Convolution Encoder
Eye Motion Matters for 3D Face Reconstruction
3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation
토론
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