Summary of Taming Latent Diffusion Model For Neural Radiance Field Inpainting, by Chieh Hubert Lin et al.
Taming Latent Diffusion Model for Neural Radiance Field Inpainting
by Chieh Hubert Lin, Changil Kim, Jia-Bin Huang, Qinbo Li, Chih-Yao Ma, Johannes Kopf, Ming-Hsuan Yang, Hung-Yu Tseng
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 a novel framework for neural radiance field (NeRF) inpainting, addressing the challenges of synthesizing reasonable geometry in completely uncovered regions and mitigating textural shifts due to auto-encoding errors. The authors introduce two key contributions: tempering the diffusion model’s stochasticity with per-scene customization and mitigating the textural shift with masked adversarial training. They also demonstrate that commonly used pixel and perceptual losses are harmful for NeRF inpainting, and instead propose rigorous experiments using state-of-the-art techniques. The framework yields state-of-the-art results on various real-world scenes, showcasing its effectiveness in 3D reconstruction from multi-view images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NeRF is a way to reconstruct 3D objects from many pictures taken from different angles. But sometimes, these reconstructions can be messy and hard to edit. This paper tries to solve this problem by making the edits more reliable and realistic. They do this by adjusting the “randomness” of the editing process and using special training techniques to avoid weird texture changes. The authors also show that some popular ways to measure how good an edited picture is can actually be bad for NeRF editing. By testing their ideas on real-world scenes, they get better results than before. |
Keywords
» Artificial intelligence » Diffusion model
