VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context
  Learning
Authors: name: Baolu Li, name: Yiming Zhang, name: Qinghe Wang, name: Liqian Ma, name: Xiaoyu Shi, name: Xintao Wang, name: Pengfei Wan, name: Zhenfei Yin, name: Yunzhi Zhuge, name: Huchuan Lu, name: Xu Jia

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.