VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context
  Learning
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.
6902c4f472739622ee92a70b
Baolu Li
claimed_verified
2025-10-30T14:28:21.321Z
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Libaolu
user
8ruceLi
6902c4f472739622ee92a70c
Yiming Zhang
claimed_verified
2025-10-30T14:28:23.673Z
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Yiming  Zhang
user
zh1ym
6902c4f472739622ee92a70d
Qinghe Wang
claimed_verified
2025-10-30T14:28:26.744Z
646f3418a6a58aa29505fd30
https://cdn-avatars.huggingface.co/v1/production/uploads/646f3418a6a58aa29505fd30/1z13rnpb6rsUgQsYumWPg.png
QINGHE WANG
user
DecoderWQH666
6902c4f472739622ee92a70e
Liqian Ma
6902c4f472739622ee92a70f
Xiaoyu Shi
6902c4f472739622ee92a710
Xintao Wang
6902c4f472739622ee92a711
Pengfei Wan
6902c4f472739622ee92a712
Zhenfei Yin
6902c4f472739622ee92a713
Yunzhi Zhuge
6902c4f472739622ee92a714
Huchuan Lu
6902c4f472739622ee92a715
Xu Jia
VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context
  Learning
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.
VFXMaster is a unified framework for generating VFX effects using in-context learning, enabling generalization to unseen effects and efficient adaptation from a single video.
in-context learning in-context conditioning in-context attention mask effect imitation one-shot effect adaptation