WithEveryone Reports Face Fidelity Gains Over GPT-Image-2 Baseline
TL;DR
- The paper reports 97.3% identity coverage with a 2.8% duplication rate when placing up to ten specified people in a single image.
- Face similarity rises from 0.462 for a GPT-Image-2 baseline to 0.499, with copy-paste artifacts falling from 0.169 to 0.055.
- The framework injects each identity as an addressed token, predicts a structured identity-layout plan, then renders that plan as a visual condition.
A framework called WithEveryone can place up to ten specified people in a single generated image, its authors report, with identity fidelity metrics running ahead of a GPT-Image-2 baseline. Hengyuan Xu and seven co-authors posted the preprint to arXiv on August 20, 2026.
The reported metrics: 97.3% coverage of requested identities with a 2.8% duplication rate, face similarity rising from 0.462 to 0.499, and copy-paste artifacts falling from 0.169 to 0.055. The method "injects each selected identity as an addressed token, predicts a structured identity–layout plan, and renders the plan as a visual condition," the abstract states.
The published comparison runs only against GPT-Image-2, and the abstract reports aggregate scores without per-identity or per-image breakdowns.
Originally reported by paper
Read the original article →Original headline: WithEveryone Places 10 Named People in One Image, Outperforms GPT-Image-2 on Face Fidelity