  The generation of high-quality human images through text-to-image (T2I)
methods is a significant yet challenging task. Distinct from general image
generation, human image synthesis must satisfy stringent criteria related to
human pose, anatomy, and alignment with textual prompts, making it particularly
difficult to achieve realistic results. Recent advancements in T2I generation
based on diffusion models have shown promise, yet challenges remain in meeting
human-specific preferences. In this paper, we introduce a novel approach
tailored specifically for human image generation utilizing Direct Preference
Optimization (DPO). Specifically, we introduce an efficient method for
constructing a specialized DPO dataset for training human image generation
models without the need for costly human feedback. We also propose a modified
loss function that enhances the DPO training process by minimizing artifacts
and improving image fidelity. Our method demonstrates its versatility and
effectiveness in generating human images, including personalized text-to-image
generation. Through comprehensive evaluations, we show that our approach
significantly advances the state of human image generation, achieving superior
results in terms of natural anatomies, poses, and text-image alignment.
