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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/2508.07597 |
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| _version_ | 1866911105992884224 |
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| author | Zhang, Yuang Cheng, Junqi Zhao, Haoyu Gu, Jiaxi Zou, Fangyuan Lu, Zenghui Shu, Peng |
| author_facet | Zhang, Yuang Cheng, Junqi Zhao, Haoyu Gu, Jiaxi Zou, Fangyuan Lu, Zenghui Shu, Peng |
| contents | Over-the-shoulder dialogue videos are essential in films, short dramas, and advertisements, providing visual variety and enhancing viewers' emotional connection. Despite their importance, such dialogue scenes remain largely underexplored in video generation research. The main challenges include maintaining character consistency across different shots, creating a sense of spatial continuity, and generating long, multi-turn dialogues within limited computational budgets. Here, we present ShoulderShot, a framework that combines dual-shot generation with looping video, enabling extended dialogues while preserving character consistency. Our results demonstrate capabilities that surpass existing methods in terms of shot-reverse-shot layout, spatial continuity, and flexibility in dialogue length, thereby opening up new possibilities for practical dialogue video generation. Videos and comparisons are available at https://shouldershot.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07597 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ShoulderShot: Generating Over-the-Shoulder Dialogue Videos Zhang, Yuang Cheng, Junqi Zhao, Haoyu Gu, Jiaxi Zou, Fangyuan Lu, Zenghui Shu, Peng Computer Vision and Pattern Recognition Artificial Intelligence Over-the-shoulder dialogue videos are essential in films, short dramas, and advertisements, providing visual variety and enhancing viewers' emotional connection. Despite their importance, such dialogue scenes remain largely underexplored in video generation research. The main challenges include maintaining character consistency across different shots, creating a sense of spatial continuity, and generating long, multi-turn dialogues within limited computational budgets. Here, we present ShoulderShot, a framework that combines dual-shot generation with looping video, enabling extended dialogues while preserving character consistency. Our results demonstrate capabilities that surpass existing methods in terms of shot-reverse-shot layout, spatial continuity, and flexibility in dialogue length, thereby opening up new possibilities for practical dialogue video generation. Videos and comparisons are available at https://shouldershot.github.io. |
| title | ShoulderShot: Generating Over-the-Shoulder Dialogue Videos |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2508.07597 |