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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.05180 |
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| _version_ | 1866929618433343488 |
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| author | Utintu, Chaitat Chowdhury, Pinaki Nath Sain, Aneeshan Koley, Subhadeep Bhunia, Ayan Kumar Song, Yi-Zhe |
| author_facet | Utintu, Chaitat Chowdhury, Pinaki Nath Sain, Aneeshan Koley, Subhadeep Bhunia, Ayan Kumar Song, Yi-Zhe |
| contents | Video colour editing is a crucial task for content creation, yet existing solutions either require painstaking frame-by-frame manipulation or produce unrealistic results with temporal artefacts. We present a practical, training-free framework that makes precise video colour editing accessible through an intuitive interface while maintaining professional-quality output. Our key insight is that by decoupling spatial and temporal aspects of colour editing, we can better align with users' natural workflow -- allowing them to focus on precise colour selection in key frames before automatically propagating changes across time. We achieve this through a novel technical framework that combines: (i) a simple point-and-click interface merging grid-based colour selection with automatic instance segmentation for precise spatial control, (ii) bidirectional colour propagation that leverages inherent video motion patterns, and (iii) motion-aware blending that ensures smooth transitions even with complex object movements. Through extensive evaluation on diverse scenarios, we demonstrate that our approach matches or exceeds state-of-the-art methods while eliminating the need for training or specialized hardware, making professional-quality video colour editing accessible to everyone. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05180 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DreamColour: Controllable Video Colour Editing without Training Utintu, Chaitat Chowdhury, Pinaki Nath Sain, Aneeshan Koley, Subhadeep Bhunia, Ayan Kumar Song, Yi-Zhe Computer Vision and Pattern Recognition Video colour editing is a crucial task for content creation, yet existing solutions either require painstaking frame-by-frame manipulation or produce unrealistic results with temporal artefacts. We present a practical, training-free framework that makes precise video colour editing accessible through an intuitive interface while maintaining professional-quality output. Our key insight is that by decoupling spatial and temporal aspects of colour editing, we can better align with users' natural workflow -- allowing them to focus on precise colour selection in key frames before automatically propagating changes across time. We achieve this through a novel technical framework that combines: (i) a simple point-and-click interface merging grid-based colour selection with automatic instance segmentation for precise spatial control, (ii) bidirectional colour propagation that leverages inherent video motion patterns, and (iii) motion-aware blending that ensures smooth transitions even with complex object movements. Through extensive evaluation on diverse scenarios, we demonstrate that our approach matches or exceeds state-of-the-art methods while eliminating the need for training or specialized hardware, making professional-quality video colour editing accessible to everyone. |
| title | DreamColour: Controllable Video Colour Editing without Training |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.05180 |