探索基于解耦形状-纹理表示的三维感知寿命人脸老化
Exploring 3D-aware Lifespan Face Aging via Disentangled Shape-Texture Representations
AI总结：
本文提出3D-STD，一种基于三维感知的形状-纹理解耦人脸老化网络，通过三维重建解耦形状与纹理，并采用经验模态分解生成高保真纹理，实现最先进的老化效果及三维老化结果。
摘要：
现有的人脸老化方法通常侧重于建模纹理老化，或使用纠缠的形状-纹理表示来实现人脸老化。然而，形状和纹理是相互影响人类面部老化过程的两个不同因素。本文提出了3D-STD，一种新颖的基于三维感知的形状-纹理解耦人脸老化网络，通过三维人脸重建将面部图像显式解耦为形状和纹理表示。此外，为了促进高保真纹理合成，我们提出了一种基于经验模态分解（EMD）的新型纹理生成方法。大量的定性和定量实验表明，我们的方法在形状和纹理变换方面达到了最先进的性能。此外，我们的方法支持生成合理的三维人脸老化结果，这是当前方法很少能够实现的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
