Using Gaussian Splats to Create High-Fidelity Facial Geometry and Texture

TL;DR

This paper introduces a method using Gaussian Splatting for high-fidelity facial geometry and texture reconstruction from limited images, enabling realistic rendering and editing in standard graphics pipelines.

Contribution

The approach leverages Gaussian Splatting with segmentation and surface constraints to reconstruct detailed facial geometry and textures from few images, improving flexibility and fidelity.

Findings

Reconstructed facial geometry from only 11 images.

Generated high-resolution, relightable textures.

Enabled scene asset creation with minimal image data.

Abstract

We leverage increasingly popular three-dimensional neural representations in order to construct a unified and consistent explanation of a collection of uncalibrated images of the human face. Our approach utilizes Gaussian Splatting, since it is more explicit and thus more amenable to constraints than NeRFs. We leverage segmentation annotations to align the semantic regions of the face, facilitating the reconstruction of a neutral pose from only 11 images (as opposed to requiring a long video). We soft constrain the Gaussians to an underlying triangulated surface in order to provide a more structured Gaussian Splat reconstruction, which in turn informs subsequent perturbations to increase the accuracy of the underlying triangulated surface. The 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.