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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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/JEQW6WZBcmjAWwODiDkYA.jpeg
Libaolu
8ruceLi
user
8ruceLi
6902c4f472739622ee92a70c
Yiming Zhang
claimed_verified
2025-10-30T14:28:23.673Z
66f2748bfa0f7d682326c834
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Yiming  Zhang
zh1ym
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
Qinghew
user
Qinghew
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