DreamReward: Text-to-3D Generation with Human Preference
<|reference_start|>DreamReward: Text-to-3D Generation with Human Preference: 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.<|reference_end|>
@article{ye2024dreamreward:,
    title={DreamReward: Text-to-3D Generation with Human Preference},
    author={Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou
  Wang, Yueqi Duan, Jun Zhu},
    journal={arXiv preprint arXiv:2403.14613},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14613},
    primaryClass={cs.CV cs.CL cs.LG}
}