FitMe: Deep Photorealistic 3D Morphable Model Avatars
This addresses the need for photorealistic, relightable avatars for end-user applications, offering a faster alternative to implicit reconstructions.
The paper tackles the problem of acquiring high-fidelity, renderable human avatars from single or multiple images by introducing FitMe, a facial reflectance model and differentiable rendering optimization pipeline, achieving state-of-the-art reflectance acquisition and identity preservation with results in one minute.
In this paper, we introduce FitMe, a facial reflectance model and a differentiable rendering optimization pipeline, that can be used to acquire high-fidelity renderable human avatars from single or multiple images. The model consists of a multi-modal style-based generator, that captures facial appearance in terms of diffuse and specular reflectance, and a PCA-based shape model. We employ a fast differentiable rendering process that can be used in an optimization pipeline, while also achieving photorealistic facial shading. Our optimization process accurately captures both the facial reflectance and shape in high-detail, by exploiting the expressivity of the style-based latent representation and of our shape model. FitMe achieves state-of-the-art reflectance acquisition and identity preservation on single "in-the-wild" facial images, while it produces impressive scan-like results, when given multiple unconstrained facial images pertaining to the same identity. In contrast with recent implicit avatar reconstructions, FitMe requires only one minute and produces relightable mesh and texture-based avatars, that can be used by end-user applications.