SynthFaces: Balanced Large Scale Human Dataset

Sergey Karpukhin, Denis Bobkov, Kirill Morozov, Aibek Alanov

SynthFaces: Balanced Large Scale Human Dataset: 1 upvotes on Hugging Face Daily Papers, #32 of 32 papers on 2026-11-06. Day-by-day upvote history.

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It had 1 upvotes by its first day, 1 upvotes by the next day, 1 upvotes by day 3, 1 upvotes by day 7, and has 1 now. It ranked #199 of 207 papers in its week and #324 of 340 in its month. Its code repository has 2 GitHub stars.

The pursuit of fair and equitable AI in human-centric tasks is critically hindered by the inherent biases and privacy risks of real-world datasets, which offer imbalanced representations of identity and often contain sensitive personal information. Consequently, state-of-the-art generative models trained on this skewed data exhibit unfair performance, particularly in identity-preserving generation. In this work we propose a pipeline for controllable generation of dataset with diverse faces for generative models training and finetuning. We achieve this by expanding foundational Arc2Face diffusion model with a flow matching generative model in the manifold of identity embeddings. We demonstrate that such approach achieves both goals of generative fidelity and label diversity not present in real human-centric datasets. As a result we collect and further contextualize a highly balanced identity-centric dataset of humans named SynthFaces. By design, our released images and identities are synthetic, reducing direct reliance on real personal images; we empirically check identity separation with a nearest-neighbor non-leakage metric. We purpose it for training and tuning of fair models for various downstream tasks, and showcase its usability in finetuning an existing adapter-based method for personalized generation.

Paper page on Hugging Face · arXiv

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