Summary of Isotropic3d: Image-to-3d Generation Based on a Single Clip Embedding, by Pengkun Liu et al.
Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding
by Pengkun Liu, Yikai Wang, Fuchun Sun, Jiafang Li, Hang Xiao, Hongxiang Xue, Xinzhou Wang
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes Isotropic3D, a novel image-to-3D generation pipeline that takes only an image CLIP embedding as input. The approach uses Score Distillation Sampling (SDS) and combines it with a two-stage diffusion model fine-tuning process. The first stage involves fine-tuning a text-to-3D diffusion model by substituting its text encoder with an image encoder, allowing the model to acquire image-to-image capabilities. The second stage uses Explicit Multi-view Attention (EMA) to combine noisy multi-view images with a noise-free reference image as an explicit condition. The CLIP embedding is used throughout the process, and reference images are discarded after fine-tuning. Isotropic3D generates mutually consistent 2D views and a 3D model with improved symmetry, texture, and geometry compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Isotropic3D is a new way to create 3D models from 2D images. The idea is to take an image and use it to create multiple 3D views that are consistent with each other. This is done by using special computer algorithms that learn from examples and can correct mistakes as they go. The result is a 3D model that looks more like the original image, but also has improved details like texture and geometry. |
Keywords
* Artificial intelligence * Attention * Diffusion model * Distillation * Embedding * Encoder * Fine tuning
