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.

翻译：通过文本到图像（T2I）方法生成高质量人像是一项重要且具有挑战性的任务。与通用图像生成不同，人像合成必须满足与人体姿态、解剖结构以及与文本提示对齐相关的严格标准，这使得生成逼真结果尤为困难。基于扩散模型的T2I生成技术近期取得了进展，但在满足人类特定偏好方面仍存在挑战。本文提出一种专门针对人像生成的新方法，该方法利用直接偏好优化（DPO）。具体而言，我们引入了一种高效的方法来构建专门的DPO数据集，用于训练人像生成模型，而无需昂贵的人工反馈。我们还提出了一种改进的损失函数，通过最小化伪影并提高图像保真度来增强DPO训练过程。我们的方法在生成人像（包括个性化文本到图像生成）方面展示了其多功能性和有效性。通过综合评估，我们表明该方法显著推进了人像生成的现有水平，在自然解剖结构、姿态和图文对齐方面取得了优异的结果。