FitDiff: Robust monocular 3D facial shape and reflectance estimation using Diffusion Models

The remarkable progress in 3D face reconstruction has resulted in high-detail and photorealistic facial representations. Recently, Diffusion Models have revolutionized the capabilities of generative methods by achieving far better performance than GANs. In this work, we present FitDiff, a diffusion-based 3D facial avatar generative model. This model accurately generates relightable facial avatars, utilizing an identity embedding extracted from an "in-the-wild" 2D facial image. Our multi-modal diffusion model concurrently outputs facial reflectance maps (diffuse and specular albedo and normals) and shapes, showcasing great generalization capabilities. It is solely trained on an annotated subset of a public facial dataset, paired with 3D reconstructions. We revisit the typical 3D facial fitting approach by guiding a reverse diffusion process using perceptual and face recognition losses. Being the first LDM conditioned on face recognition embeddings, FitDiff reconstructs relightable human avatars, that can be used as-is in common rendering engines, starting only from an unconstrained facial image, and achieving state-of-the-art performance.