Summary of Dreamreward: Text-to-3d Generation with Human Preference, by Junliang Ye et al.
DreamReward: Text-to-3D Generation with Human Preference
by Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou Wang, Yueqi Duan, Jun Zhu
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a framework called DreamReward, designed to learn and improve text-to-3D models by incorporating human preference feedback. The authors collect 25k expert comparisons through a systematic annotation pipeline, including rating and ranking. They then develop Reward3D, the first general-purpose text-to-3D human preference reward model, which effectively encodes human preferences. Building upon this model, the authors propose DreamFL, a direct tuning algorithm to optimize multi-view diffusion models with a redefined scorer. Theoretical analysis and extensive experiment comparisons demonstrate the effectiveness of DreamReward in generating high-fidelity and 3D consistent results that align well with human intention. This research has significant potential for learning from human feedback to improve text-to-3D models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about using computers to create 3D pictures from written descriptions. Right now, these computer programs don’t always do a great job of creating what people want. The authors of this paper came up with a new way for computers to learn and get better at making 3D pictures that people like. They did this by asking experts to compare lots of different 3D pictures and giving feedback on which ones are the best. This helped them create a special computer program called Reward3D, which is good at figuring out what people want. The authors also came up with a new way for computers to use this information to make even better 3D pictures. Overall, this research shows that by asking for human feedback, we can teach computers to be much better at creating 3D pictures that people love. |
Keywords
* Artificial intelligence * Diffusion
