DreamReward: Text-to-3D Generation with Human Preference
Junliang Ye Fangfu Liu Qixiu Li Zhengyi Wang Yikai Wang Xinzhou Wang Yueqi Duan Jun Zhu
3D content creation from text prompts has shown remarkable success recently.
However, current text-to-3D methods often generate 3D results that do not align
well with human preferences. In this paper, we present a comprehensive
framework, coined DreamReward, to learn and improve text-to-3D models from
human preference feedback. To begin with, we collect 25k expert comparisons
based on a systematic annotation pipeline including rating and ranking. Then,
we build Reward3D -- the first general-purpose text-to-3D human preference
reward model to effectively encode human preferences. Building upon the 3D
reward model, we finally perform theoretical analysis and present the Reward3D
Feedback Learning (DreamFL), a direct tuning algorithm to optimize the
multi-view diffusion models with a redefined scorer. Grounded by theoretical
proof and extensive experiment comparisons, our DreamReward successfully
generates high-fidelity and 3D consistent results with significant boosts in
prompt alignment with human intention. Our results demonstrate the great
potential for learning from human feedback to improve text-to-3D models.