Paper Detail
Hengyuan Xu, Qixun Wang, Yiji Cheng, Miles Yang, Zhao Zhong, Wei Cheng, Xingjun Ma, Yu-gang Jiang
Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every reference to a distinct person and location, while training-time identity losses must establish correspondence among several noisy predicted faces. We introduce WithEveryone, a unified framework for generating group images up to ten reference identities. WithEveryone injects each selected identity as an addressed token, predicts a structured identity--layout plan, and renders the plan as a visual condition. Its key objective, Layout-Grounded ID Loss, uses annotated face regions to supervise the intended identities directly, avoiding unstable embedding-based face matching; ID Representation Forcing additionally trains a prediction for each identity before image synthesis. On an identity-disjoint benchmark, WithEveryone achieves the highest target-context identity similarity, improving face similarity from 0.462 for GPT-Image-2 to 0.499, while reducing copy-paste artifacts from 0.169 to 0.055. It further covers 97.3\% of the requested identities with a duplicate rate of only 2.8\%. These results show that explicit identity--layout grounding enables identity-preserving generation to scale to larger groups without relying on direct reference-face copying.
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@article{xu2026witheveryone,
title = {WithEveryone: Unified Planning and Identity Grounding for Group Image Generation},
author = {Hengyuan Xu and Qixun Wang and Yiji Cheng and Miles Yang and Zhao Zhong and Wei Cheng and Xingjun Ma and Yu-gang Jiang},
year = {2026},
abstract = {Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every reference to a distinct person and location, while training-time identity losses must establish correspondence among several noisy predicted faces. We introduce WithEveryone, a unified framework for generating group images up to ten reference identities. WithEveryone injects each selected identity as an addressed token, pr},
url = {https://arxiv.org/abs/2608.20336},
keywords = {cs.CV, identity-preserving image generation, addressed token, identity--layout plan, Layout-Grounded ID Loss, ID Representation Forcing, identity-disjoint benchmark, target-context identity similarity, code available, huggingface daily},
eprint = {2608.20336},
archiveprefix = {arXiv},
}
{}