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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.01586 |
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| _version_ | 1866912461131612160 |
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| author | Sadihin, Bryan Constantine Wang, Michael Hua Chua, Shei Pern Su, Hang |
| author_facet | Sadihin, Bryan Constantine Wang, Michael Hua Chua, Shei Pern Su, Hang |
| contents | The production of high-quality 2D animation is highly labor-intensive process, as animators are currently required to draw and color a large number of frames by hand. We present SketchColour, the first sketch-to-colour pipeline for 2D animation built on a diffusion transformer (DiT) backbone. By replacing the conventional U-Net denoiser with a DiT-style architecture and injecting sketch information via lightweight channel-concatenation adapters accompanied with LoRA finetuning, our method natively integrates conditioning without the parameter and memory bloat of a duplicated ControlNet, greatly reducing parameter count and GPU memory usage. Evaluated on the SAKUGA dataset, SketchColour outperforms previous state-of-the-art video colourization methods across all metrics, despite using only half the training data of competing models. Our approach produces temporally coherent animations with minimal artifacts such as colour bleeding or object deformation. Our code is available at: https://bconstantine.github.io/SketchColour . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01586 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SketchColour: Channel Concat Guided DiT-based Sketch-to-Colour Pipeline for 2D Animation Sadihin, Bryan Constantine Wang, Michael Hua Chua, Shei Pern Su, Hang Computer Vision and Pattern Recognition The production of high-quality 2D animation is highly labor-intensive process, as animators are currently required to draw and color a large number of frames by hand. We present SketchColour, the first sketch-to-colour pipeline for 2D animation built on a diffusion transformer (DiT) backbone. By replacing the conventional U-Net denoiser with a DiT-style architecture and injecting sketch information via lightweight channel-concatenation adapters accompanied with LoRA finetuning, our method natively integrates conditioning without the parameter and memory bloat of a duplicated ControlNet, greatly reducing parameter count and GPU memory usage. Evaluated on the SAKUGA dataset, SketchColour outperforms previous state-of-the-art video colourization methods across all metrics, despite using only half the training data of competing models. Our approach produces temporally coherent animations with minimal artifacts such as colour bleeding or object deformation. Our code is available at: https://bconstantine.github.io/SketchColour . |
| title | SketchColour: Channel Concat Guided DiT-based Sketch-to-Colour Pipeline for 2D Animation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.01586 |