WithEveryone: Unified Planning and Identity Grounding for Group Image Generation

• URL: http://arxiv.org/abs/2608.20336v1
• Date: Thu, 20 Aug 2026 17:59:53 GMT
• Title: WithEveryone: Unified Planning and Identity Grounding for Group Image Generation
• Abstract summary: We introduce WithEveryone, a unified framework for generating group images up to ten reference identities.<n>WithEveryone injects each selected identity as an addressed token, predicts a structured identity--plan, and renders the plan as a visual condition.<n>It 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.
• Score: 60.528070962852716
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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, 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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