arXiv:2606.26947v1 Announce Type: cross Abstract: While personalized image generation has achieved remarkable progress, multi-reference image generation (MRIG) remains a challenging task. Most existing benchmarks fail to adequately evaluate complex MRIG scenarios, hindering further progress in this area. To better assess model performance on complex MRIG tasks, we introduce OmniRef-Bench, a benchmark that covers complex combinations of reference image types and a large number of reference images. Evaluations on OmniRef-Bench show that mainstream open-source models struggle in complex MRIG scen
The proliferation of personalized image generation models highlights the current limitations in multi-reference image generation, driving the need for more robust benchmarks and development.
Improving multi-reference image generation is crucial for advanced AI agentic systems and complex content creation, impacting various sectors from design to simulation.
The introduction of OmniRef-Bench provides a standardized, complex evaluation tool that can accelerate progress in multi-reference image generation, potentially raising the bar for model capabilities.
- · AI model developers
- · Creative industries
- · Research institutions
- · AI agents
- · Models with poor MRIG capabilities
- · Traditional content creation methods
New benchmark accelerates research and development in multi-reference image generation.
Improved MRIG capabilities enable more sophisticated and complex AI-generated content and environments.
Enhanced visual generation tools could transform fields requiring high-fidelity, customizable visual assets, from entertainment to product design, and further empower AI agents.
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Read at arXiv cs.AI