We present a framework for completing high-fidelity 3D facial UV maps from single-face image. Despite the success of Generative Adversarial Networks (GANs) in this area, generating accurate UV maps from in-the-wild images remains challenging. Our approach involves a novel network called “Map and Edit” that combines a 2D generative model and a 3D prior to explicitly control the generation of multi-view faces. We use an indirect method to address domain gap issues between rendered and real images, which improves the identity consistency of the generated multi-view facial images. We also leverage synthesized multi-view images and predicted 3D information to produce texture-rich and high-resolution facial UV maps. Our model is self-supervised and does not require manual annotations or datasets. Experimental results demonstrate the effectiveness of our framework in reconstructing high-fidelity UV maps with accurate, fine details. Overall, our approach provides a promising solution to the challenges of 3D facial UV map completion from in-the-wild images.

中文翻译：

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实现现实世界中高保真面部 UV 贴图生成

我们提出了一个从单脸图像完成高保真 3D 面部 UV 贴图的框架。尽管生成对抗网络 (GAN) 在这一领域取得了成功，但从野外图像生成准确的 UV 地图仍然具有挑战性。我们的方法涉及一种名为“映射和编辑”的新颖网络，该网络结合了 2D 生成模型和 3D 模型，可以显式控制多视图人脸的生成。我们使用间接方法来解决渲染图像和真实图像之间的域差距问题，从而提高了生成的多视图面部图像的身份一致性。我们还利用合成的多视图图像和预测的 3D 信息来生成纹理丰富的高分辨率面部 UV 贴图。我们的模型是自我监督的，不需要手动注释或数据集。实验结果证明了我们的框架在重建具有准确、精细细节的高保真 UV 地图方面的有效性。总的来说，我们的方法为解决从野外图像完成 3D 面部 UV 贴图的挑战提供了一个有前景的解决方案。