DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation
Yuang Peng Yuxin Cui Haomiao Tang Zekun Qi Runpei Dong Jing Bai Chunrui Han Zheng Ge Xiangyu Zhang Shu-Tao Xia
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 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.
personalized image generation, subject-driven image generation, personalization, image generation, benchmarking, human-aligned evaluation
This paper present DreamBench++, a human-aligned benchmark that advanced multimodal GPT models automate.