VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context Learning
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918177902952448 |
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| author | Li, Baolu Zhang, Yiming Wang, Qinghe Ma, Liqian Shi, Xiaoyu Wang, Xintao Wan, Pengfei Yin, Zhenfei Zhuge, Yunzhi Lu, Huchuan Jia, Xu |
| author_facet | Li, Baolu Zhang, Yiming Wang, Qinghe Ma, Liqian Shi, Xiaoyu Wang, Xintao Wan, Pengfei Yin, Zhenfei Zhuge, Yunzhi Lu, Huchuan Jia, Xu |
| contents | Visual effects (VFX) are crucial to the expressive power of digital media, yet their creation remains a major challenge for generative AI. Prevailing methods often rely on the one-LoRA-per-effect paradigm, which is resource-intensive and fundamentally incapable of generalizing to unseen effects, thus limiting scalability and creation. To address this challenge, we introduce VFXMaster, the first unified, reference-based framework for VFX video generation. It recasts effect generation as an in-context learning task, enabling it to reproduce diverse dynamic effects from a reference video onto target content. In addition, it demonstrates remarkable generalization to unseen effect categories. Specifically, we design an in-context conditioning strategy that prompts the model with a reference example. An in-context attention mask is designed to precisely decouple and inject the essential effect attributes, allowing a single unified model to master the effect imitation without information leakage. In addition, we propose an efficient one-shot effect adaptation mechanism to boost generalization capability on tough unseen effects from a single user-provided video rapidly. Extensive experiments demonstrate that our method effectively imitates various categories of effect information and exhibits outstanding generalization to out-of-domain effects. To foster future research, we will release our code, models, and a comprehensive dataset to the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context Learning Li, Baolu Zhang, Yiming Wang, Qinghe Ma, Liqian Shi, Xiaoyu Wang, Xintao Wan, Pengfei Yin, Zhenfei Zhuge, Yunzhi Lu, Huchuan Jia, Xu Computer Vision and Pattern Recognition Visual effects (VFX) are crucial to the expressive power of digital media, yet their creation remains a major challenge for generative AI. Prevailing methods often rely on the one-LoRA-per-effect paradigm, which is resource-intensive and fundamentally incapable of generalizing to unseen effects, thus limiting scalability and creation. To address this challenge, we introduce VFXMaster, the first unified, reference-based framework for VFX video generation. It recasts effect generation as an in-context learning task, enabling it to reproduce diverse dynamic effects from a reference video onto target content. In addition, it demonstrates remarkable generalization to unseen effect categories. Specifically, we design an in-context conditioning strategy that prompts the model with a reference example. An in-context attention mask is designed to precisely decouple and inject the essential effect attributes, allowing a single unified model to master the effect imitation without information leakage. In addition, we propose an efficient one-shot effect adaptation mechanism to boost generalization capability on tough unseen effects from a single user-provided video rapidly. Extensive experiments demonstrate that our method effectively imitates various categories of effect information and exhibits outstanding generalization to out-of-domain effects. To foster future research, we will release our code, models, and a comprehensive dataset to the community. |
| title | VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.25772 |