Summary of Instruct 4d-to-4d: Editing 4d Scenes As Pseudo-3d Scenes Using 2d Diffusion, by Linzhan Mou et al.
Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion
by Linzhan Mou, Jun-Kun Chen, 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)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Instruct 4D-to-4D, a novel approach that achieves 4D awareness and spatial-temporal consistency for 2D diffusion models in generating high-quality instruction-guided dynamic scene editing results. Traditional 2D diffusion models have limitations when applied to dynamic scene editing, resulting in inconsistency due to their frame-by-frame editing methodology. To address this, the authors treat a 4D scene as a pseudo-3D scene, decoupling it into two sub-problems: achieving temporal consistency in video editing and applying edits to the pseudo-3D scene. The Instruct-Pix2Pix (IP2P) model is enhanced with an anchor-aware attention module for batch processing and consistent editing. Additionally, optical flow-guided appearance propagation and depth-based projection are integrated for precise frame-to-frame editing. Iterative editing achieves convergence, resulting in spatially and temporally consistent editing results with enhanced detail and sharpness compared to prior art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers edit videos better. Right now, video editors can only change one frame at a time, which doesn’t work well for complex scenes like movies or games. The authors came up with a new way to treat 4D (3D plus time) scenes as if they were 3D, making it easier to edit them consistently. They used an existing model called Instruct-Pix2Pix and improved it by adding some new features that help with batch processing and editing. The results are much better than before, with more detail and sharpness. |
Keywords
* Artificial intelligence * Attention * Diffusion * Optical flow
