title: Boost Your Own Human Image Generation Model via Direct Preference
    Optimization with AI Feedback
summary: 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.
paper_authors._id: 666e9b96bc840e67481f20f3
paper_authors.name: Sanghyeon Na
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-02T11:59:46.873Z
paper_authors._id: 6662b3ee280fb71780b85ef8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/wDr9CDlL40_gp-NBRm7gB.png
paper_authors.fullname: Sanghyeon Na
paper_authors.type: user
paper_authors.user: sanghyeonna
paper_authors._id: 666e9b96bc840e67481f20f4
paper_authors.name: Yonggyu Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T14:05:41.152Z
paper_authors._id: 6523df09f1491fbf6a93c6c8
paper_authors.avatarUrl: /avatars/886859aaffc7ab855f8e022c36ebb495.svg
paper_authors.fullname: Yonggyu Kim
paper_authors.type: user
paper_authors.user: yonggyuarthur
paper_authors._id: 666e9b96bc840e67481f20f5
paper_authors.name: Hyunjoon Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-03T14:05:48.108Z
paper_authors._id: 6786d01913da730e75fb88e3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/WALoMzoiaIGPJf6lXhT_Q.png
paper_authors.fullname: Hyunjoon Lee
paper_authors.type: user
paper_authors.user: hyunjoonlee12
paper_title: Boost Your Own Human Image Generation Model via Direct Preference
    Optimization with AI Feedback
paper_summary: 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.
paper_ai_summary: A Direct Preference Optimization (DPO) approach is introduced for high-quality human image generation from text, enhancing image fidelity and meeting human-specific criteria through modified loss functions and efficient dataset construction.
paper_ai_keywords: text-to-image (T2I) diffusion models Direct Preference Optimization (DPO) human image generation human-specific preferences anatomies poses text-image alignment specialized DPO dataset modified loss function artifacts image fidelity