Despite tremendous success has been achieved in faithfully reconstructing face shapes from single images, recovering accurate local details still remains challenging. Previous works propose reprojection-based methods to improve the performance of detail recovering – they render a textured 3D shape into an image and make it approximate to the input during iterations. However, details from textures and shapes are mixed in the rendered image when minimizing the re-projection loss, which leads to limitations in detail recovery. To address this issue, we propose a novel 3D face reconstruction framework that 1) uses a coarse-medium-fine strategy to capture details while preserving the global shape, 2) disentangles details from the texture to enhance local accuracy, and 3) applies a phased optimization to recover details over multiple scales. Experiments demonstrate the capability of our framework to reconstruct high-fidelity face shapes with accurate, fine details.

尽管在从单个图像忠实地重建面部形状方面取得了巨大成功，但恢复准确的局部细节仍然具有挑战性。以前的工作提出了基于重投影的方法来提高细节恢复的性能——它们将带纹理的 3D 形状渲染到图像中，并在迭代过程中使其接近输入。然而，当最小化重投影损失时，来自纹理和形状的细节在渲染图像中混合，这导致细节恢复的限制。为了解决这个问题，我们提出了一种新颖的 3D 人脸重建框架，该框架 1) 使用粗-中-细策略来捕获细节，同时保留全局形状，2) 从纹理中解开细节以提高局部精度，以及 3) 应用分阶段优化以在多个尺度上恢复细节。