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arXiv · 2403.05239

Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation

Junyan Wang·Zhenhong Sun·Zhiyu Tan·Xuanbai Chen·Weihua Chen·Hao Li·Cheng Zhang·Yang Song
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Abstract

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}.

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Junyan Wang, Zhenhong Sun, Zhiyu Tan, Xuanbai Chen, Weihua Chen, Hao Li, Cheng Zhang, Yang Song. 2024-03-08. Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation. https://arxiv.org/abs/2403.05239

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