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| <!DOCTYPE html> | ||
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| content="Arc2Face: A Foundation Model of Human Faces"> | ||
| <meta name="keywords" content="Arc2Face, ID-embeddings, Stable Diffusion"> | ||
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| <title>Arc2Face: A Foundation Model of Human Faces</title> | ||
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| <h1 class="title is-1 publication-title">Arc2Face: A Foundation Model of Human Faces</h1> | ||
| <div class="is-size-5 publication-authors"> | ||
| <span class="author-block"> | ||
| <a href="https://foivospar.github.io/">Foivos Paraperas Papantoniou</a>, | ||
| </span> | ||
| <span class="author-block"> | ||
| <a href="https://alexlattas.com">Alexandros Lattas</a>, | ||
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| <a href="https://moschoglou.com/">Stylianos Moschoglou</a>, | ||
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| <span class="author-block"> | ||
| <a href="https://jiankangdeng.github.io/">Jiankang Deng</a>, | ||
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| <span class="author-block"> | ||
| <a href="https://bernhard-kainz.com/">Bernhard Kainz</a>, | ||
| </span> | ||
| <span class="author-block"> | ||
| <a href="https://www.imperial.ac.uk/people/s.zafeiriou">Stefanos Zafeiriou</a> | ||
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| <span class="author-block">Imperial College London, UK</span> | ||
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| <span>arXiv</span> | ||
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| <h2 class="title is-3">Abstract</h2> | ||
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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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| TBD | ||
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| TBD | ||
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| <pre><code></code></pre> | ||
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| The source code of this website is borrowed from <a | ||
| href="https://github.com/nerfies/nerfies.github.io">Nerfies</a>. | ||
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