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IC-Effect: Precise and Efficient Video Effects Editing via In-Context Learning

Yuanhang Li, Yiren Song, Junzhe Bai, Xinran Liang, Hu Yang, Libiao Jin, Qi Mao

arXiv:2512.15635v1cs.CVcs.AI

TL;DR

Video VFX editing must integrate effects into existing footage while preserving its background and temporal structure, a challenge compounded by difficult-to-scale paired data. IC-Effect uses DiT contextual conditioning, staged adaptation, and sparse tokenization for few-shot editing. The paper reports instruction-guided effect editing and introduces a paired dataset spanning 15 effect types.

  • Problem

    Video VFX editing requires effects to blend into existing footage while leaving the background unchanged, but existing editing methods struggle with this and high-quality paired data is difficult to produce.

    Source: §1 · p. 2

  • Method

    IC-Effect conditions a DiT on clean source-video tokens, adapts it first for general editing and then with Effect-LoRA on paired effects, and uses spatiotemporal sparse tokenization.

    Source: §1 · p. 2, §3.2 · p. 4, §3.4 · p. 4

  • Results

    IC-Effect is reported to follow effect instructions and maintain consistency with training styles while preserving background consistency in video VFX editing.

    Source: §4.2 · p. 6, §5 · p. 8

  • Takeaways & Limitations

    The paired dataset spans 15 effect types and is presented as a reproducible benchmark for video VFX editing research.

    Source: §1 · p. 2

    IC-Effect relies on high-quality paired video VFX data, which the authors say is extremely challenging to produce.

    Source: F. Limitation and Future Work · p. 14

Abstract

from arXiv · show

We propose \textbf{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.

1. Introduction

Video VFX editing must add effects while preserving the source video's background, structure, and temporal coherence, but existing approaches and scarce paired data leave the task largely unresolved. IC-Effect uses DiT contextual learning and a two-stage training strategy, alongside a paired VFX dataset.

1. Introduction

  • Existing methods struggle to preserve the source background while adding effects, and mask-based approaches depend on pixel-accurate masks that conflict with automated editing.High-quality paired VFX data is also difficult to produce, limiting training scalability.Source: §1 · p. 2
  • IC-Effect uses a source video as a clean contextual condition and combines general editing adaptation with Effect-LoRA learning from a small set of paired VFX samples.Spatiotemporal sparse tokenization with position correction is introduced to reduce computation while preserving key features and alignment.Source: §1 · p. 2
  • The proposed dataset covers 15 representative effect types and pairs source and edited videos with textual descriptions and spatiotemporal annotations.Samples are aligned in viewpoint, content, and motion; the authors describe the dataset as the first large-scale paired benchmark for video VFX editing.Source: §1 · p. 2
  • The paper presents IC-Effect as an instruction-guided DiT framework and reports experiments on realistic effects, background preservation, and temporal consistency.Its stated contributions also include the paired dataset and sparse tokenization with position correction.Source: §1 · p. 2

2. Related Work

Related work traces video diffusion from temporal modules toward DiT models with spatiotemporal attention, while video editing and VFX generation remain distinct lines of work. IC-Effect draws on DiT in-context conditioning and sparse condition tokens to address video-effects editing and its token-cost challenge.

2.1. Diffusion-Based Video Generation and Editing

  • DiT-based video models represent videos as spatiotemporal token sequences and use 3D full attention, while existing editing approaches still fall short on VFX editing with abstract effects and scarce paired data.Earlier diffusion approaches added temporal modules or specialized spatiotemporal architectures; video-editing methods also explored source-frame injection and structural priors.Source: §2.1 · p. 2
  • Figure 2 depicts source-video tokenization and a two-stage training strategy: high-rank LoRA for general editing and instruction following, then low-rank LoRA for effect styles.Only target tokens are decoded to produce the edited video.Source: Figure 2 · p. 3

2.2. Visual Effect Generation

  • Video VFX generation methods such as OmniEffects and VFX-Creator synthesize stylized clips from scratch rather than precisely editing existing videos.The paper positions its goal as text-guided style fusion while preserving the original video's structure and motion.Source: §2.2 · p. 3

2.3. Vision In-context Learning

  • DiT in-context methods concatenate condition tokens with latent noise and jointly model them, but quadratic token cost makes long conditions computationally expensive.IC-Effect instead uses spatiotemporally sparse condition tokens to reduce extra computation.Source: §2.3 · p. 3

3. Method

IC-Effect combines in-context source conditioning with two-stage editor and effect training, then adds sparse-token position correction and a paired VFX dataset.

3.1. Overall Architecture

  • A pretrained video editor learns instruction-following behavior first; low-rank Effect-LoRA then learns specific effect styles from paired examples.The first stage uses a large-scale video-editing dataset, while the second fine-tunes on a small set of paired video-effect data.Source: §3.1 · pp. 3–4

3.2. In-Context Conditioning for Video Editing

  • Clean source latents provide spatial and motion information, while causal attention lets noisy target tokens use that context without altering the clean tokens.The model encodes and concatenates source and target latents; training applies flow-matching loss only to the latent tokens.Source: §3.2 · p. 4

3.3. Effect-LoRA

  • Effect-LoRA uses a low-rank update to learn one effect’s editing style from limited paired data, reducing trainable parameters and overfitting risk.The broader Video-Editor is trained with high-rank LoRA for instruction following, while Effect-LoRA is specialized for a single effect.Source: §3.3 · p. 4

3.4. Spatiotemporal Sparse Tokenization

  • Spatiotemporal sparse tokenization reduces source-video token cost while retaining temporal motion and fine spatial details; position correction addresses sparse-token misalignment.Spatial tokens use the first frame’s noisy-latent positional encoding to avoid temporal misalignment.Source: §3.4 · pp. 4–5

3.5. Video Effects Dataset

  • VideoVFX covers 15 effect categories with over 20 curated videos, pairing source and target clips with text instructions for supervised editing.The target effects are artist-designed and exhibit precise spatiotemporal coherence.Source: §3.5 · p. 5

4. Experiment

Experiments report strong editing performance, user preference, and reduced computation with quality comparable to the fully tokenized model. The evaluation combines benchmark comparisons, qualitative results, a user study, and ablations.

4.1. Experimental Setup

  • The evaluation uses automatic video-quality, semantic-alignment, and GPT-4o measures alongside benchmarks and trained baseline comparisons.The setup describes 80 samples for Video-Editor evaluation and a separate 50-video customized effect editing benchmark.Source: §4.1 · pp. 5–6

4.2. Video VFX Editing Results

  • Qualitative results show accurate instructed effect injection, stable styles, and preserved spatiotemporal consistency in non-effect regions.With limited paired effect data, the method avoids color drift and structural distortion and reports stable performance across settings.Source: §4.2 · p. 6, Figure 3 · p. 6

4.3. Comparison with Baselines

  • IC-Effect achieves the best metrics among the compared methods on both common video editing and video effect editing.The comparison uses Table 1; GPT-4o evaluation also favors IC-Effect in structure preservation and effect accuracy.Source: §4.3 · p. 7, Table 1 · p. 7

4.4. User Study

  • Users significantly prefer IC-Effect, with the study assessing instruction adherence and source-video fidelity.The questionnaire compares results from the proposed method and a baseline in conventional editing and customized VFX editing.Source: §4.4 · pp. 7–8, Figure 5 · p. 8

4.5. Ablation Studies

  • Spatiotemporal sparse tokenization reduces computation while keeping editing quality comparable to full tokenization, provided a high-quality first frame supplies spatial detail.Without that first-frame condition, sparse tokens alone produce blotchy artifacts and blurring; the full method preserves video content and follows prompts.Source: §4.5 · p. 8, Figure 6 · p. 8
  • The ablations indicate that Video-Editor pretraining and Effect-LoRA serve complementary roles in customized VFX editing.Without pretraining, particle effects spread to all subjects rather than only the white cake; without Effect-LoRA, customized effects do not match user intent.Source: §4.5 · p. 8

5. Conclusion

The conclusion presents IC-Effect as a few-shot framework for instruction-guided video effects editing. It reports consistent advantages in effect fidelity and background consistency while introducing a 15-type effects dataset.

5. Conclusion

  • IC-Effect learns visual styles from few paired effect samples and follows textual instructions while preserving background consistency.The framework uses DiT contextual learning and a two-stage training strategy for instruction adherence and effect-style learning.Source: §5 · p. 8
  • Spatiotemporal sparse tokenization preserves source-video information while significantly reducing computational cost.Source: §5 · p. 8
  • The authors report consistent outperformance of existing methods in general and VFX editing, with superior effect fidelity and background consistency.The conclusion also introduces a dataset covering 15 video effect types as a benchmark for future research.Source: §5 · p. 8

Supplementary Material

The supplementary material adds implementation and dataset details, extended results, and a limitations discussion. It also covers instruction control, multi-effect editing, and further ablations.

Supplementary Material

  • The supplement provides additional implementation details, extended ablations, and more qualitative results.Source: Supplementary Material · p. 12
  • It describes pretraining and paired VFX datasets, flexible instruction control, multi-effect editing, causal-attention and positional-correction ablations, and limitations.It also includes extended qualitative comparisons for general video editing and video VFX editing.Source: Supplementary Material · p. 12

A.1. Training Details

The supplement specifies training and inference settings, baseline implementation sources, and evaluation procedures. Evaluation includes automatic metrics, GPT-4o ratings, and user-preference questionnaires.

A.1. Training Details

  • VideoEditor uses rank-96 LoRA training for 50k iterations; Effect-LoRA uses rank 32 for 1,000 iterations, both with AdamW and learning rate 1×10−4.Training uses 224 × 416 videos with 81 frames, batch size 2, and weight decay 1 × 10−2 on four and two A800 GPUs, respectively.Source: §A.1 · p. 12

A.2. Inference Details

  • Inference generates at 480×832, using up to 81 source frames, classifier-free guidance scale 5.0, and 50 denoising steps.For source videos shorter than 81 frames, the selected source length is the first 4n + 1 frames.Source: §A.2 · p. 12

A.3. Implementation Details of Baselines

  • Common-editing baselines use official codes and weights, while VFX competitors are trained on the same paired dataset.The authors use official training code or faithful re-implementations for VFX comparisons.Source: §A.3 · p. 12

A.4. Evaluation Details

  • Automatic evaluation combines CLIP and ViCLIP alignment, VBench quality measures, and GPT-4o ratings of preservation and editing effectiveness.User questionnaires compare instruction following, structural fidelity, effect consistency for VFX, and overall preference.Source: §A.4 · pp. 12–13

B.1. Datasets of Pretrained Video-Editor

The paper builds paired general-editing and VFX datasets, then shows that prompt changes control effect attributes and directions and that mixed training supports precise multi-effect edits without cross-effect leakage.

B.1. Datasets of Pretrained Video-Editor

  • The paired dataset is constructed to pre-train VideoEditor with strong instruction-following capability and covers addition, removal, replacement, attribute modification, and style transfer, with approximately 50,000 high-quality samples; videos are resized to 480 × 832.Source: §B.1 · p. 13

B.2. Video VFX Datasets

  • The VFX dataset pairs source videos, target effects, and editing instructions across 15 effect types, standardized to 480 × 832 and five seconds at 16 FPS.The effects include animated characters, anime duplication, rings, graffiti, lightning, aura, fireworks, flames, architectural growth and bounce, line effects, particles, and light-particle traversal.Source: §B.2 · p. 13

C. Instruction Control for Video VFX Editing

D. Video Multi VFX Editing

  • Mixed training supports instruction-targeted multi-effect insertion without cross-effect leakage while preserving non-edited regions.The authors attribute this capability to the architecture and high-quality training data.Source: D. Video Multi VFX Editing · p. 14

E. Additional Ablation Studies

The ablation study reports that removing positional encoding correction or causal attention degrades edited-video quality, while the complete model has higher editing accuracy and visual consistency.

E. Additional Ablation Studies

  • The complete model demonstrates higher editing accuracy and stronger visual consistency in the ablation comparison.Table 3 presents the quantitative ablation of positional encoding correction and causal attention, but the supplied passage gives no cell values.Source: E. Additional Ablation Studies · p. 14
  • Removing positional encoding correction introduces artifacts and blurring and alters source-video information.Source: E. Additional Ablation Studies · p. 14
  • Without causal attention, latent noise contaminates the spatiotemporally sparse tokens and produces severe artifacts.Source: E. Additional Ablation Studies · p. 14

F. Limitation and Future Work

IC-Effect relies on high-quality paired video VFX data, which is extremely challenging to produce; the authors propose extracting target effects from reference VFX videos as future work.

F. Limitation and Future Work

G.1. Additional Results of Video VFX Editing

Additional results report coherent, well-integrated VFX edits across effect categories, prompt-following edits that preserve spatial structure and temporal coherence, and strong performance on common editing tasks.

G.1. Additional Results of Video VFX Editing

  • Across diverse effect categories, IC-Effect follows textual prompts and produces visually coherent, well-integrated edits.Source: §G.1 · p. 14

G.2. Additional Qualitative Comparison of Video VFX Editing

  • In additional video VFX editing quantitative comparisons with baseline methods, IC-Effect consistently follows textual prompts to edit source videos while preserving their spatial structure and temporal coherence.Source: §G.2 · p. 14

G.3. Additional Qualitative Comparison of Common Video Editing

  • Across addition, removal, attribute modification, replacement, and style transfer, IC-Effect is reported to follow instructions, make precise edits, and preserve non-edited regions.The passage identifies Figures 17–21 as comparisons with existing methods.Source: §G.3 · p. 14
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