IC-Effect: Precise and Efficient Video Effects Editing via In-Context Learning
We propose IC-Effect, an instruction-guided, DiT-based framework for few-shot video VFX editing that synthesizes complex effects (\eg flames, particles and cartoon characters) while strictly preserving spatial and temporal consistency. Video VFX editing is highly challenging because injected effects must blend seamlessly with the background, the background must remain entirely unchanged, and effect patterns must be learned efficiently from limited paired data. However, existing video editing models fail to satisfy these requirements. IC-Effect leverages the source video as clean contextual conditions, exploiting the contextual learning capability of DiT models to achieve precise background preservation and natural effect injection. A two-stage training strategy, consisting of general editing adaptation followed by effect-specific learning via Effect-LoRA, ensures strong instruction following and robust effect modeling. To further improve efficiency, we introduce spatiotemporal sparse tokenization, enabling high fidelity with substantially reduced computation. We also release a paired VFX editing dataset spanning 15 high-quality visual styles. Extensive experiments show that IC-Effect delivers high-quality, controllable, and temporally consistent VFX editing, opening new possibilities for video creation.
6943902a542d62d58a7bf7a0
Yuanhang Li
claimed_verified
2025-12-18T07:59:34.872Z
64aa2210e04e7f92245f54d2
https://cdn-avatars.huggingface.co/v1/production/uploads/64aa2210e04e7f92245f54d2/OE43T22bLWBVgmqcJyUtu.png
Li
kotion
user
kotion
6943902a542d62d58a7bf7a1
Yiren Song
6943902a542d62d58a7bf7a2
Junzhe Bai
6943902a542d62d58a7bf7a3
Xinran Liang
6943902a542d62d58a7bf7a4
Hu Yang
6943902a542d62d58a7bf7a5
Libiao Jin
6943902a542d62d58a7bf7a6
Qi Mao
claimed_verified
2025-12-18T07:59:37.087Z
6388a7e98a5dbe2f3dc61faa
https://cdn-avatars.huggingface.co/v1/production/uploads/6388a7e98a5dbe2f3dc61faa/Zj8pR7BG_wOQjsYzosb5p.jpeg
Qi Mao
HelenMao
user
HelenMao
IC-Effect: Precise and Efficient Video Effects Editing via In-Context Learning
We propose IC-Effect, an instruction-guided, DiT-based framework for few-shot video VFX editing that synthesizes complex effects (\eg flames, particles and cartoon characters) while strictly preserving spatial and temporal consistency. Video VFX editing is highly challenging because injected effects must blend seamlessly with the background, the background must remain entirely unchanged, and effect patterns must be learned efficiently from limited paired data. However, existing video editing models fail to satisfy these requirements. IC-Effect leverages the source video as clean contextual conditions, exploiting the contextual learning capability of DiT models to achieve precise background preservation and natural effect injection. A two-stage training strategy, consisting of general editing adaptation followed by effect-specific learning via Effect-LoRA, ensures strong instruction following and robust effect modeling. To further improve efficiency, we introduce spatiotemporal sparse tokenization, enabling high fidelity with substantially reduced computation. We also release a paired VFX editing dataset spanning 15 high-quality visual styles. Extensive experiments show that IC-Effect delivers high-quality, controllable, and temporally consistent VFX editing, opening new possibilities for video creation.
IC-Effect is a DiT-based framework for few-shot video VFX editing that synthesizes complex effects while maintaining spatial and temporal consistency through contextual learning and sparse tokenization.
DiT-based framework video VFX editing spatial consistency temporal consistency contextual learning Effect-LoRA spatiotemporal sparse tokenization