Summary of L4gm: Large 4d Gaussian Reconstruction Model, by Jiawei Ren et al.
L4GM: Large 4D Gaussian Reconstruction Model
by Jiawei Ren, Kevin Xie, Ashkan Mirzaei, Hanxue Liang, Xiaohui Zeng, Karsten Kreis, Ziwei Liu, Antonio Torralba, Sanja Fidler, Seung Wook Kim, Huan Ling
First submitted to arxiv on: 14 Jun 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 The paper presents the first-ever 4D Large Reconstruction Model (L4GM) capable of generating animated objects from single-view video input. L4GM employs a novel dataset, Objaverse, containing 44K diverse objects with 110K animations rendered in 48 viewpoints, resulting in 12M videos and 300M frames. The model builds upon the LGM, a pretrained 3D Large Reconstruction Model, and incorporates temporal self-attention layers for consistency across time and per-timestep multiview rendering loss for training. L4GM upsamples its representation to achieve temporal smoothness and utilizes an interpolation model for high-quality animated 3D assets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make moving objects from just one video view. It uses a special dataset with lots of different objects and animations, which helps the model learn how to do this task well. The model works by taking single-view videos and turning them into 4D representations that can be upscaled to higher framerates for smoother animation. This model is really good at making animated 3D assets, even when trained on fake data. |
Keywords
» Artificial intelligence » Self attention
