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-- This paper presents Arc2Face, an identity-conditioned face foundation model, - which, given the ArcFace embedding of a person, can generate diverse photo-realistic - images with an unparalleled degree of face similarity than existing models. Despite - previous attempts to decode face recognition features into detailed images, we find - that common high-resolution datasets (e.g. FFHQ) lack sufficient identities to reconstruct - any subject. To that end, we meticulously upsample a significant portion of the WebFace42M - database, the largest public dataset for face recognition (FR). Arc2Face builds upon a - pretrained Stable Diffusion model, yet adapts it to the task of ID-to-face generation, - conditioned solely on ID vectors. Deviating from recent works that combine ID with text - embeddings for zero-shot personalization of text-to-image models, we emphasize on the - compactness of FR features, which can fully capture the essence of the human face, as - opposed to hand-crafted prompts. Crucially, text-augmented models struggle to decouple - identity and text, usually necessitating some description of the given face to achieve - satisfactory similarity. Arc2Face, however, only needs the discriminative features of - ArcFace to guide the generation, offering a robust prior for a plethora of tasks where - ID consistency is of paramount importance. As an example, we train a FR model on synthetic - images from our model and achieve superior performance to existing synthetic datasets. -
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