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Summary of Bootstrap3d: Improving Multi-view Diffusion Model with Synthetic Data, by Zeyi Sun et al.


Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data

by Zeyi Sun, Tong Wu, Pan Zhang, Yuhang Zang, Xiaoyi Dong, Yuanjun Xiong, Dahua Lin, Jiaqi Wang

First submitted to arxiv on: 31 May 2024

Categories

  • Main: Computer Vision and Pattern Recognition (cs.CV)
  • Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Multimedia (cs.MM)

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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
A novel framework called Bootstrap3D is proposed to address the challenge of generating high-quality 3D objects for training multi-view diffusion models. The framework employs a data generation pipeline that combines 2D and video diffusion models with a fine-tuned 3D-aware MV-LLaVA model to generate multi-view images based on constructed text prompts, and filter out low-quality data. This pipeline is used to generate 1 million high-quality synthetic multi-view images with dense descriptive captions. Additionally, the paper introduces a Training Timestep Reschedule (TTR) strategy that leverages the denoising process to learn multi-view consistency while maintaining the original 2D diffusion prior. Experimental results demonstrate that Bootstrap3D can generate high-quality multi-view images with superior aesthetic quality, image-text alignment, and maintained view consistency.
Low GrooveSquid.com (original content) Low Difficulty Summary
Bootstrap3D is a new way to make 3D objects for computers. Right now, it’s hard to train machines to create good-looking 3D pictures because we don’t have enough real-world examples. To fix this problem, scientists created a special program that can make lots of fake but realistic 3D images with words attached to them. This helps the computer learn how to make better 3D pictures. The new approach is called Bootstrap3D and it uses a combination of old and new techniques to create millions of fake 3D images. This could lead to better computers that can make more realistic 3D pictures.

Keywords

» Artificial intelligence  » Alignment  » Diffusion