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.
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Junliang Ye
claimed_verified
2024-03-25T09:44:07.989Z
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yejunliang
user
yejunliang23
65fcf1dbc3f470c772d92e5b
Fangfu Liu
admin_assigned
2024-03-22T08:48:56.419Z
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Fangfu Liu
user
Liuff23
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Qixiu Li
65fcf1dbc3f470c772d92e5d
Zhengyi Wang
admin_assigned
2024-03-22T08:49:32.174Z
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Zhengyi Wang
user
Zhengyi
65fcf1dbc3f470c772d92e5e
Yikai Wang
65fcf1dbc3f470c772d92e5f
Xinzhou Wang
admin_assigned
2024-03-22T08:50:52.014Z
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Xinzhou Wang
user
zz7379
65fcf1dbc3f470c772d92e60
Yueqi Duan
65fcf1dbc3f470c772d92e61
Jun Zhu
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.
A framework called DreamReward uses human preference feedback to enhance text-to-3D generation, producing high-fidelity and aligned 3D models.
3D content creation text-to-3D human preference DreamReward expert comparisons annotation pipeline Reward3D human preference reward model Reward3D Feedback Learning DreamFL multi-view diffusion models redefined scorer prompt alignment