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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.18364 |
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| _version_ | 1866913706744479744 |
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| author | Pu, Yifan Zhao, Yiming Tang, Zhicong Yin, Ruihong Ye, Haoxing Yuan, Yuhui Chen, Dong Bao, Jianmin Zhang, Sirui Wang, Yanbin Liang, Lin Wang, Lijuan Li, Ji Li, Xiu Lian, Zhouhui Huang, Gao Guo, Baining |
| author_facet | Pu, Yifan Zhao, Yiming Tang, Zhicong Yin, Ruihong Ye, Haoxing Yuan, Yuhui Chen, Dong Bao, Jianmin Zhang, Sirui Wang, Yanbin Liang, Lin Wang, Lijuan Li, Ji Li, Xiu Lian, Zhouhui Huang, Gao Guo, Baining |
| contents | Multi-layer image generation is a fundamental task that enables users to isolate, select, and edit specific image layers, thereby revolutionizing interactions with generative models. In this paper, we introduce the Anonymous Region Transformer (ART), which facilitates the direct generation of variable multi-layer transparent images based on a global text prompt and an anonymous region layout. Inspired by Schema theory suggests that knowledge is organized in frameworks (schemas) that enable people to interpret and learn from new information by linking it to prior knowledge.}, this anonymous region layout allows the generative model to autonomously determine which set of visual tokens should align with which text tokens, which is in contrast to the previously dominant semantic layout for the image generation task. In addition, the layer-wise region crop mechanism, which only selects the visual tokens belonging to each anonymous region, significantly reduces attention computation costs and enables the efficient generation of images with numerous distinct layers (e.g., 50+). When compared to the full attention approach, our method is over 12 times faster and exhibits fewer layer conflicts. Furthermore, we propose a high-quality multi-layer transparent image autoencoder that supports the direct encoding and decoding of the transparency of variable multi-layer images in a joint manner. By enabling precise control and scalable layer generation, ART establishes a new paradigm for interactive content creation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18364 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation Pu, Yifan Zhao, Yiming Tang, Zhicong Yin, Ruihong Ye, Haoxing Yuan, Yuhui Chen, Dong Bao, Jianmin Zhang, Sirui Wang, Yanbin Liang, Lin Wang, Lijuan Li, Ji Li, Xiu Lian, Zhouhui Huang, Gao Guo, Baining Computer Vision and Pattern Recognition Multi-layer image generation is a fundamental task that enables users to isolate, select, and edit specific image layers, thereby revolutionizing interactions with generative models. In this paper, we introduce the Anonymous Region Transformer (ART), which facilitates the direct generation of variable multi-layer transparent images based on a global text prompt and an anonymous region layout. Inspired by Schema theory suggests that knowledge is organized in frameworks (schemas) that enable people to interpret and learn from new information by linking it to prior knowledge.}, this anonymous region layout allows the generative model to autonomously determine which set of visual tokens should align with which text tokens, which is in contrast to the previously dominant semantic layout for the image generation task. In addition, the layer-wise region crop mechanism, which only selects the visual tokens belonging to each anonymous region, significantly reduces attention computation costs and enables the efficient generation of images with numerous distinct layers (e.g., 50+). When compared to the full attention approach, our method is over 12 times faster and exhibits fewer layer conflicts. Furthermore, we propose a high-quality multi-layer transparent image autoencoder that supports the direct encoding and decoding of the transparency of variable multi-layer images in a joint manner. By enabling precise control and scalable layer generation, ART establishes a new paradigm for interactive content creation. |
| title | ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.18364 |