SofGAN: A Portrait Image Generator with Dynamic Styling

نویسندگان

چکیده

Recently, Generative Adversarial Networks (GANs) have been widely used for portrait image generation. However, in the latent space learned by GANs, different attributes, such as pose, shape, and texture style, are generally entangled, making explicit control of specific attributes difficult. To address this issue, we propose a SofGAN generator to decouple portraits into two subspaces: geometry space. The codes sampled from subspaces fed network branches separately, one generate 3D with canonical other textures. aligned geometries also come semantic part segmentation, encoded occupancy field (SOF). SOF allows rendering consistent 2D segmentation maps at arbitrary views, which then fused generated texturemaps stylized photo using our instance-wise module. Through extensive experiments, show that system can high-quality images independently controllable attributes. method generalizes well various applications, appearance-consistent facial animation dynamic styling.

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ژورنال

عنوان ژورنال: ACM Transactions on Graphics

سال: 2022

ISSN: ['0730-0301', '1557-7368']

DOI: https://doi.org/10.1145/3470848