  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}.
