Dreambench++: A Human-aligned Benchmark For Personalized Image Generation
Peng Yuang, Cui Yuxin, Tang Haomiao, Qi Zekun, Dong Runpei, Bai Jing, Han Chunrui, Ge Zheng, Zhang Xiangyu, Xia Shu-tao. Arxiv 2024
[Paper]
Personalized image generation holds great promise in assisting humans in everyday work and life due to its impressive function in creatively generating personalized content. 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 automated by advanced multimodal GPT models. 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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