# SynthFaces: Balanced Large Scale Human Dataset

**Authors:** Sergey Karpukhin, Denis Bobkov, Kirill Morozov, Aibek Alanov  
**Lab:** National Research University Higher School of Economics · **Published:** 28 Oct 2026 · **Upvotes:** 1  
**Links:** [arXiv](https://arxiv.org/abs/2610.40119) · [PDF](https://arxiv.org/pdf/2610.40119) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.40119.html)

## Abstract

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.

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