利用高斯溅实现高保真人脸几何与纹理重建

我们利用日益流行的三维神经表示方法，构建对一系列未标定人脸图像的统一且一致的解释。我们的方法采用高斯溅（Gaussian Splatting），因为其更显式，因而更易于施加约束。我们利用分割标注对人脸语义区域进行对齐，从而实现仅需11张图像即可重建中性姿态（不像需要长时间视频那样）。我们对高斯溅施加软性约束，使其依赖于底层三角网格曲面，以获得更结构化的高斯溅重建，这进而指导后续扰动以提高底层三角网格曲面的精度。 resulting triangulated surface can then be used in a standard graphics pipeline. In addition, and perhaps most impactful, we show how accurate geometry enables the Gaussian Splats to be transformed into texture space where they can be treated as a view-dependent neural texture. This allows one to use high visual fidelity Gaussian Splatting on any asset in a scene without the need to modify any other asset or any other aspect (geometry, lighting, renderer, etc.) of the graphics pipeline. We utilize a relightable Gaussian model to disentangle texture from lighting in order to obtain a delit high-resolution albedo texture that is also readily usable in a standard graphics pipeline. The flexibility of our system allows for training with disparate images, even with incompatible lighting, facilitating robust regularization. Finally, we demonstrate the efficacy of our approach by illustrating its use in a text-driven asset creation pipeline.