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DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation

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arxiv 2406.16855 v2 pith:FMTGIPTU submitted 2024-06-24 cs.CV

Yuang Peng , Yuxin Cui , Haomiao Tang , Zekun Qi , Runpei Dong , Jing Bai , Chunrui Han , Zheng Ge
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Xiangyu Zhang Shu-Tao Xia
This is my paper · ORCID
classification cs.CV
keywords human-aligneddreambenchpersonalizedbenchmarkevaluationsgenerationhumansimage
verification ladder T0 review T1 audit T2 compute T3 formal
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Personalized image generation holds great promise in assisting humans in everyday work and life due to its impressive ability to creatively generate personalized content across various contexts. However, current evaluations either are automated but misalign with humans or require human evaluations that are time-consuming and expensive. In this work, we present DreamBench++, a human-aligned benchmark that advanced multimodal GPT models automate. Specifically, we systematically design the prompts to let GPT be both human-aligned and self-aligned, empowered with task reinforcement. Further, we construct a comprehensive dataset comprising diverse images and prompts. By benchmarking 7 modern generative models, we demonstrate that DreamBench++ results in significantly more human-aligned evaluation, helping boost the community with innovative findings.

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Cited by 12 Pith papers

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