DrawVideo: Generating Long Video from Storyboard Keyframe Sketches
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918518297985024 |
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| author | Xu, Chuanzhi Liang, Huiqi Shi, Bang Zhang, Huiming Xiao, Yifan Lin, Guangcheng Chen, Haodong Qu, Qiang Lu, Zhicheng Cai, Weidong |
| author_facet | Xu, Chuanzhi Liang, Huiqi Shi, Bang Zhang, Huiming Xiao, Yifan Lin, Guangcheng Chen, Haodong Qu, Qiang Lu, Zhicheng Cai, Weidong |
| contents | Long video generation requires high-fidelity synthesis, coherent narrative structure, and user control over extended time spans. Existing text-to-video methods often rely on a single long prompt, limiting control over pose, composition, layout, and motion. We propose DrawVideo, a sketch-guided, storyboard-driven framework for controllable long-video generation. DrawVideo decomposes long videos into independently controllable shots, each defined by a black-and-white sketch, an appearance prompt, and a motion prompt. The sketch controls pose and layout, the appearance prompt defines identity, scene, and style, and the motion prompt guides temporal dynamics. DrawVideo follows a hierarchical 'global multi-shot, local single-sketch' strategy: it first generates a structure-aligned reference keyframe, then expands the motion prompt into derivative keyframes representing action states, and finally synthesizes clips between adjacent keyframes to build each shot. We also introduce SketchLongVideo, the first dataset for sketch-guided text-to-long-video generation, constructed from animation videos via shot detection, keyframe extraction, vision-language recognition, prompt decomposition, and sketch conversion. Experiments show that DrawVideo achieves strong structural controllability, appearance consistency, visual stability, and coherent long-video generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23508 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DrawVideo: Generating Long Video from Storyboard Keyframe Sketches Xu, Chuanzhi Liang, Huiqi Shi, Bang Zhang, Huiming Xiao, Yifan Lin, Guangcheng Chen, Haodong Qu, Qiang Lu, Zhicheng Cai, Weidong Graphics Artificial Intelligence Computer Vision and Pattern Recognition Multimedia Image and Video Processing Long video generation requires high-fidelity synthesis, coherent narrative structure, and user control over extended time spans. Existing text-to-video methods often rely on a single long prompt, limiting control over pose, composition, layout, and motion. We propose DrawVideo, a sketch-guided, storyboard-driven framework for controllable long-video generation. DrawVideo decomposes long videos into independently controllable shots, each defined by a black-and-white sketch, an appearance prompt, and a motion prompt. The sketch controls pose and layout, the appearance prompt defines identity, scene, and style, and the motion prompt guides temporal dynamics. DrawVideo follows a hierarchical 'global multi-shot, local single-sketch' strategy: it first generates a structure-aligned reference keyframe, then expands the motion prompt into derivative keyframes representing action states, and finally synthesizes clips between adjacent keyframes to build each shot. We also introduce SketchLongVideo, the first dataset for sketch-guided text-to-long-video generation, constructed from animation videos via shot detection, keyframe extraction, vision-language recognition, prompt decomposition, and sketch conversion. Experiments show that DrawVideo achieves strong structural controllability, appearance consistency, visual stability, and coherent long-video generation. |
| title | DrawVideo: Generating Long Video from Storyboard Keyframe Sketches |
| topic | Graphics Artificial Intelligence Computer Vision and Pattern Recognition Multimedia Image and Video Processing |
| url | https://arxiv.org/abs/2605.23508 |