Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation
<|reference_start|>Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation: Vanilla text-to-image diffusion models struggle with generating accurate human images, commonly resulting in imperfect anatomies such as unnatural postures or disproportionate limbs.Existing methods address this issue mostly by fine-tuning the model with extra images or adding additional controls -- human-centric priors such as pose or depth maps -- during the image generation phase. This paper explores the integration of these human-centric priors directly into the model fine-tuning stage, essentially eliminating the need for extra conditions at the inference stage. We realize this idea by proposing a human-centric alignment loss to strengthen human-related information from the textual prompts within the cross-attention maps. To ensure semantic detail richness and human structural accuracy during fine-tuning, we introduce scale-aware and step-wise constraints within the diffusion process, according to an in-depth analysis of the cross-attention layer. Extensive experiments show that our method largely improves over state-of-the-art text-to-image models to synthesize high-quality human images based on user-written prompts. Project page: \url{https://hcplayercvpr2024.github.io}.<|reference_end|>
@article{wang2024towards,
    title={Towards Effective Usage of Human-Centric Priors in Diffusion Models for
  Text-based Human Image Generation},
    author={Junyan Wang, Zhenhong Sun, Zhiyu Tan, Xuanbai Chen, Weihua Chen, Hao
  Li, Cheng Zhang, Yang Song},
    journal={arXiv preprint arXiv:2403.05239},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05239},
    primaryClass={cs.CV cs.AI cs.LG}
}