Boost Your Own Human Image Generation Model via Direct Preference
  Optimization with AI Feedback
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.
666e9b96bc840e67481f20f3
Sanghyeon Na
claimed_verified
2024-07-02T11:59:46.873Z
6662b3ee280fb71780b85ef8
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/wDr9CDlL40_gp-NBRm7gB.png
Sanghyeon Na
user
sanghyeonna
666e9b96bc840e67481f20f4
Yonggyu Kim
admin_assigned
2025-04-03T14:05:41.152Z
6523df09f1491fbf6a93c6c8
/avatars/886859aaffc7ab855f8e022c36ebb495.svg
Yonggyu Kim
user
yonggyuarthur
666e9b96bc840e67481f20f5
Hyunjoon Lee
admin_assigned
2025-04-03T14:05:48.108Z
6786d01913da730e75fb88e3
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/WALoMzoiaIGPJf6lXhT_Q.png
Hyunjoon Lee
user
hyunjoonlee12
Boost Your Own Human Image Generation Model via Direct Preference
  Optimization with AI Feedback
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.
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.
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