Summary of Consistdreamer: 3d-consistent 2d Diffusion For High-fidelity Scene Editing, by Jun-kun Chen et al.
ConsistDreamer: 3D-Consistent 2D Diffusion for High-Fidelity Scene Editing
by Jun-Kun Chen, Samuel Rota Bulò, Norman Müller, Lorenzo Porzi, Peter Kontschieder, Yu-Xiong Wang
First submitted to arxiv on: 13 Jun 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 The proposed ConsistDreamer framework enables high-fidelity instruction-guided scene editing by lifting 2D diffusion models with 3D awareness and consistency. The approach introduces three strategies to augment the input of the 2D model, including surrounding views as context-rich input, generating 3D-consistent noise, and self-supervised training for consistency enforcement. The framework achieves state-of-the-art performance for scene editing across various scenes and instructions, particularly in large-scale indoor scenes from ScanNet++. Notably, ConsistDreamer successfully edits complex patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ConsistDreamer is a new way to edit 3D scenes using computer models. Right now, these models can only make simple changes, but this new approach lets them do much more complicated things. The idea is to give the model more information about what it’s editing, like what’s around the scene and how it should look in 3D. This makes the editing process much better at creating realistic scenes. The team tested their method on many different kinds of scenes and found that it works really well. |
Keywords
» Artificial intelligence » Diffusion » Self supervised
