DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909975731765248 |
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| author | Liu, Jiawei Li, Junqiao Deng, Jiangfan Li, Gen Zhou, Siyu Fang, Zetao Lao, Shanshan Deng, Zengde Zhu, Jianing Ma, Tingting Li, Jiayi Wang, Yunqiu He, Qian Wu, Xinglong |
| author_facet | Liu, Jiawei Li, Junqiao Deng, Jiangfan Li, Gen Zhou, Siyu Fang, Zetao Lao, Shanshan Deng, Zengde Zhu, Jianing Ma, Tingting Li, Jiayi Wang, Yunqiu He, Qian Wu, Xinglong |
| contents | The "one-shot" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real-world constraints. Although emerging video generation models offer a virtual alternative, existing approaches typically rely on naive clip concatenation, which frequently fails to maintain visual smoothness and temporal coherence. In this paper, we introduce DreaMontage, a comprehensive framework designed for arbitrary frame-guided generation, capable of synthesizing seamless, expressive, and long-duration one-shot videos from diverse user-provided inputs. To achieve this, we address the challenge through three primary dimensions. (i) We integrate a lightweight intermediate-conditioning mechanism into the DiT architecture. By employing an Adaptive Tuning strategy that effectively leverages base training data, we unlock robust arbitrary-frame control capabilities. (ii) To enhance visual fidelity and cinematic expressiveness, we curate a high-quality dataset and implement a Visual Expression SFT stage. In addressing critical issues such as subject motion rationality and transition smoothness, we apply a Tailored DPO scheme, which significantly improves the success rate and usability of the generated content. (iii) To facilitate the production of extended sequences, we design a Segment-wise Auto-Regressive (SAR) inference strategy that operates in a memory-efficient manner. Extensive experiments demonstrate that our approach achieves visually striking and seamlessly coherent one-shot effects while maintaining computational efficiency, empowering users to transform fragmented visual materials into vivid, cohesive one-shot cinematic experiences. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_21252 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation Liu, Jiawei Li, Junqiao Deng, Jiangfan Li, Gen Zhou, Siyu Fang, Zetao Lao, Shanshan Deng, Zengde Zhu, Jianing Ma, Tingting Li, Jiayi Wang, Yunqiu He, Qian Wu, Xinglong Computer Vision and Pattern Recognition The "one-shot" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real-world constraints. Although emerging video generation models offer a virtual alternative, existing approaches typically rely on naive clip concatenation, which frequently fails to maintain visual smoothness and temporal coherence. In this paper, we introduce DreaMontage, a comprehensive framework designed for arbitrary frame-guided generation, capable of synthesizing seamless, expressive, and long-duration one-shot videos from diverse user-provided inputs. To achieve this, we address the challenge through three primary dimensions. (i) We integrate a lightweight intermediate-conditioning mechanism into the DiT architecture. By employing an Adaptive Tuning strategy that effectively leverages base training data, we unlock robust arbitrary-frame control capabilities. (ii) To enhance visual fidelity and cinematic expressiveness, we curate a high-quality dataset and implement a Visual Expression SFT stage. In addressing critical issues such as subject motion rationality and transition smoothness, we apply a Tailored DPO scheme, which significantly improves the success rate and usability of the generated content. (iii) To facilitate the production of extended sequences, we design a Segment-wise Auto-Regressive (SAR) inference strategy that operates in a memory-efficient manner. Extensive experiments demonstrate that our approach achieves visually striking and seamlessly coherent one-shot effects while maintaining computational efficiency, empowering users to transform fragmented visual materials into vivid, cohesive one-shot cinematic experiences. |
| title | DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.21252 |