DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909292578209792 |
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| author | Hu, Xiaobin Peng, Xu Luo, Donghao Ji, Xiaozhong Peng, Jinlong Jiang, Zhengkai Zhang, Jiangning Jin, Taisong Wang, Chengjie Ji, Rongrong |
| author_facet | Hu, Xiaobin Peng, Xu Luo, Donghao Ji, Xiaozhong Peng, Jinlong Jiang, Zhengkai Zhang, Jiangning Jin, Taisong Wang, Chengjie Ji, Rongrong |
| contents | Due to the difficulty and labor-consuming nature of getting highly accurate or matting annotations, there only exists a limited amount of highly accurate labels available to the public. To tackle this challenge, we propose a DiffuMatting which inherits the strong Everything generation ability of diffusion and endows the power of "matting anything". Our DiffuMatting can 1). act as an anything matting factory with high accurate annotations 2). be well-compatible with community LoRAs or various conditional control approaches to achieve the community-friendly art design and controllable generation. Specifically, inspired by green-screen-matting, we aim to teach the diffusion model to paint on a fixed green screen canvas. To this end, a large-scale greenscreen dataset (Green100K) is collected as a training dataset for DiffuMatting. Secondly, a green background control loss is proposed to keep the drawing board as a pure green color to distinguish the foreground and background. To ensure the synthesized object has more edge details, a detailed-enhancement of transition boundary loss is proposed as a guideline to generate objects with more complicated edge structures. Aiming to simultaneously generate the object and its matting annotation, we build a matting head to make a green color removal in the latent space of the VAE decoder. Our DiffuMatting shows several potential applications (e.g., matting-data generator, community-friendly art design and controllable generation). As a matting-data generator, DiffuMatting synthesizes general object and portrait matting sets, effectively reducing the relative MSE error by 15.4% in General Object Matting and 11.4% in Portrait Matting tasks. The dataset is released in our project page at \url{https://diffumatting.github.io}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06168 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation Hu, Xiaobin Peng, Xu Luo, Donghao Ji, Xiaozhong Peng, Jinlong Jiang, Zhengkai Zhang, Jiangning Jin, Taisong Wang, Chengjie Ji, Rongrong Computer Vision and Pattern Recognition Artificial Intelligence Due to the difficulty and labor-consuming nature of getting highly accurate or matting annotations, there only exists a limited amount of highly accurate labels available to the public. To tackle this challenge, we propose a DiffuMatting which inherits the strong Everything generation ability of diffusion and endows the power of "matting anything". Our DiffuMatting can 1). act as an anything matting factory with high accurate annotations 2). be well-compatible with community LoRAs or various conditional control approaches to achieve the community-friendly art design and controllable generation. Specifically, inspired by green-screen-matting, we aim to teach the diffusion model to paint on a fixed green screen canvas. To this end, a large-scale greenscreen dataset (Green100K) is collected as a training dataset for DiffuMatting. Secondly, a green background control loss is proposed to keep the drawing board as a pure green color to distinguish the foreground and background. To ensure the synthesized object has more edge details, a detailed-enhancement of transition boundary loss is proposed as a guideline to generate objects with more complicated edge structures. Aiming to simultaneously generate the object and its matting annotation, we build a matting head to make a green color removal in the latent space of the VAE decoder. Our DiffuMatting shows several potential applications (e.g., matting-data generator, community-friendly art design and controllable generation). As a matting-data generator, DiffuMatting synthesizes general object and portrait matting sets, effectively reducing the relative MSE error by 15.4% in General Object Matting and 11.4% in Portrait Matting tasks. The dataset is released in our project page at \url{https://diffumatting.github.io}. |
| title | DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2403.06168 |