Title: VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context Learning
Primary area: generative models
Keywords: Computer Vision, Diffusion Model, Controllable Generation

Abstract: Visual effects (VFX) are crucial for 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 imitation task, enabling it to reproduce diverse dynamic effects from a reference video onto a target content. Critically, it demonstrates remarkable generalization to unseen effect categories. Specifically, we design an in-context conditioning strategy that prompts the model with a reference example. We use an in-context attention mask 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.

TL;DR: We introduce VFXMaster, the first universal, reference-based framework for visual effect video generation.