Summary of Diffumatting: Synthesizing Arbitrary Objects with Matting-level Annotation, by Xiaobin Hu and Xu Peng and Donghao Luo and Xiaozhong Ji and Jinlong Peng and Zhengkai Jiang and Jiangning Zhang and Taisong Jin and Chengjie Wang and Rongrong Ji
DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation
by Xiaobin Hu, Xu Peng, Donghao Luo, Xiaozhong Ji, Jinlong Peng, Zhengkai Jiang, Jiangning Zhang, Taisong Jin, Chengjie Wang, Rongrong Ji
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 DiffuMatting model inherits the strong generative capabilities of diffusion models and adds the power to “mat anything.” This approach can act as an “anything matting factory” with high-accuracy annotations, making it well-suited for community-friendly art design and controllable generation. The model is trained on a large-scale greenscreen dataset (Green100K) and incorporates a green background control loss to ensure the synthesized object has a pure green color to distinguish the foreground and background. A detailed-enhancement of transition boundary loss is also proposed to generate objects with more complicated edge structures. To simultaneously generate the object and its matting annotation, a matting head is built to remove the green color in the latent space of the VAE decoder. The DiffuMatting model shows potential applications in generating general object and portrait matting sets, achieving a 15.4% reduction in relative MSE error for General Object Matting and an 11.4% reduction for Portrait Matting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DiffuMatting is a new way to generate objects with accurate annotations. Normally, getting these annotations takes a lot of time and effort, but this model makes it easier. It’s like having a machine that can paint anything you want on a green screen! The model uses a big dataset of images with green screens to learn how to do this. It also has special losses to make the edges of the objects look more realistic. This means we can use DiffuMatting for things like making art or creating new data sets. |
Keywords
» Artificial intelligence » Decoder » Diffusion » Latent space » Mse
