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EffectMaker: Unifying Reasoning and Generation for Customized Visual Effect Creation

Shiyuan Yang, Ruihuang Li, Jiale Tao, Shuai Shao, Qinglin Lu, Jing Liao

arXiv:2603.06014v1cs.CV

TL;DR

VFX generation remains difficult because effect-specific data are scarce and supernatural or stylized effects are challenging to model or describe precisely. EffectMaker combines MLLM reasoning with DiT in-context visual conditioning for reference-based customization and introduces EffectData with broad effect coverage. The authors report superior visual quality and effect consistency, while noting limitations on extreme motion and synthetic-data realism.

  • Problem

    VFX generation is limited by scarce effect-specific data and the difficulty of modeling or describing supernatural, abstract, and stylized effects.

  • Method

    EffectMaker uses an MLLM for semantic effect understanding and target adaptation, while a DiT uses reference-video in-context learning for visual conditioning.

  • Results

    EffectMaker achieves superior performance in replicating complex reference effects across diverse scenarios, with superior visual quality and effect consistency over existing methods.

  • Takeaways & Limitations

    EffectMaker provides a reference-based framework for customized VFX creation, while EffectData offers a large paired resource for future research and applications.

  • Takeaways & Limitations

    EffectMaker may struggle with extremely complex effects involving rapid or large motions, and synthetic training data may bias realism and diversity.

Abstract

from arXiv · show

Visual effects (VFX) are essential for enhancing the expressiveness and creativity of video content, yet producing high-quality effects typically requires expert knowledge and costly production pipelines. Existing AIGC systems face significant challenges in VFX generation due to the scarcity of effect-specific data and the inherent difficulty of modeling supernatural or stylized effects. Moreover, these approaches often require per-effect fine-tuning, which severely limits their scalability and generalization to novel VFX. In this work, we present EffectMaker, a unified reasoning-generation framework that enables reference-based VFX customization. EffectMaker employs a multimodal large language model to interpret high-level effect semantics and reason about how they should adapt to a target subject, while a diffusion transformer leverages in-context learning to capture fine-grained visual cues from reference videos. These two components form a semantic-visual dual-path guidance mechanism that enables accurate, controllable, and effect-consistent synthesis without per-effect fine-tuning. Furthermore, we construct EffectData, the largest high-quality synthetic dataset containing 130k videos across 3k VFX categories, to improve generalization and scalability. Experiments show that EffectMaker achieves superior visual quality and effect consistency over state-of-the-art baselines, offering a scalable and flexible paradigm for customized VFX generation. Project page: https://effectmaker.github.io

1. Introduction

VFX generation is valuable but difficult because effects are costly to produce, scarce in training data, and hard to describe or model when supernatural, abstract, or stylized. EffectMaker addresses these challenges with reference-based reasoning and generation, supported by a large synthetic dataset.

  • VFX enhance video expressiveness but typically require professional expertise and expensive production pipelines.
  • Existing AIGC systems struggle with supernatural and exaggerated effects because these effects are out-of-domain for real-world video distributions.
  • Text prompts often fail to capture the nuanced texture, motion dynamics, and atmosphere of abstract, multilayered, and stylistically complex effects.
  • EffectMaker uses an MLLM to reason about adapting reference effects to target subjects and a DiT to capture fine-grained reference details without effect-specific fine-tuning.
  • EffectData contains over 130k videos across 3k diverse effect categories, expanding effect coverage by an order of magnitude and providing paired annotations.
  • The semantic–visual dual-path mechanism combines multimodal understanding with DiT in-context learning for accurate and controllable VFX transfer.

2. Related work

Prior work includes general video generation, multimodal reasoning-generation, and specialized VFX generation, but existing VFX datasets and methods remain limited in coverage, flexibility, or interactivity. EffectData expands category coverage and annotation detail relative to prior datasets.

  • General video generation: Diffusion Transformers have become a major backbone for scalable, high-fidelity open-domain video generation.
  • Generation with understanding: Multimodal reasoning-generation methods include visual-token autoregressive models and LLM–diffusion hybrids, with the former typically limited in fidelity and spatial coherence.
  • Visual effect generation: MagicVFX transfers pixel-level reference content but lacks flexibility across substantially different scenes and requires extensive manual adjustment.
  • Visual effect generation: VFXCreator transfers effects through separate per-effect LoRA fine-tuning and manually crafted prompts, limiting interactive use.
  • Visual effect generation: EffectData is presented as a dataset comparison resource with expanded effect classes and detailed captions and editing instructions.
  • Visual effect generation: Existing VFX datasets cover dozens to a few hundred effect categories, whereas EffectData increases category scale by an order of magnitude and adds labels, captions, and instructions.

3. Method

EffectMaker transfers effects from a reference video to a target image by combining MLLM-based semantic reasoning with DiT-based visual conditioning. Its data pipeline uses structured VFX taxonomies and synthesized paired examples to support diverse effect generation.

  • Overview: EffectMaker formulates VFX customization as transferring effects from one reference video to a target image while preserving the reference style.
  • Overview: The architecture has an MLLM understanding component and a video DiT generation component conditioned on extracted effect representations.
  • Effect understanding: The MLLM analyzes reference effects, examines the target image, and reasons how effects should adapt to subjects with different shapes.
  • Effect generation: Semantic conditioning combines MLLM understanding and reasoning features through decoupled cross-attention to preserve modality-specific information.
  • Effect generation: Visual conditioning encodes reference and target videos jointly and uses DiT in-context learning to provide fine-grained spatial and temporal information.
  • Effect generation: The dual-stream attention scheme separates reference and target projections while allowing bidirectional attention across their combined token sequence.
  • Effect generation: Biased 3D RoPE aligns spatial positions with the target while offsetting temporal positions to separate the two videos’ encoding spaces.
  • EffectData construction: EffectData is built through subject collection and a structured taxonomy covering VFX elements, geometric patterns, and attachment regions.

4. Experiment

Experiments evaluate EffectMaker through quantitative, qualitative, ablation, scaling, and user-study analyses. Results show strong visual quality, effect consistency, reference transfer, open-set generalization, and benefits from dual conditioning and separate attention streams.

  • Qualitative results: EffectMaker produces more accurate and visually consistent closed-set effects than Wan2.2-FT and Omni-Effects, while avoiding VFXCreator’s per-effect LoRA fine-tuning.The Inflate and Ta-da cases illustrate improved transformations, and the method transfers spatial and temporal patterns such as explosion flames and flying debris.
  • Open-set comparison: On unseen portal, plastic-model, and green-tree-glowing effects, reference-guided EffectMaker achieves higher consistency than text-only or fine-tuned baselines.Omni-Effects fails on unseen categories, while Wan2.2-FT produces roughly correct but less reference-similar patterns.
  • Reference rephrasing: Reference rephrasing uses the MLLM to describe reference content and effects, then the DiT renders a new video reproducing both.This extends the framework to effect-video generation without an input image.
  • Quantitative results: EffectMaker is quantitatively evaluated on 14 OpenVFX effect classes using VQ, MQ, TA, and CAS against VFXCreator, Omni-Effects, and Wan2.2-FT.The evaluation uses 10 subject images per class and reports average scores.
  • Conditioning design: Using only semantic or visual conditioning performs worse than combining both, because visual-only conditioning mainly reproduces low-level appearance cues.The ablation tests the proposed semantic–visual dual-conditioning strategy.
  • Attention design: TA decreases from 1.24 to 0.81, CAS from 4.40 to 3.30, and RAS from 4.16 to 2.84 when dual-stream attention is replaced by single-stream attention.Separate projections better handle the distribution gap between clean reference tokens and noisy target tokens.

5. Conclusion

The paper concludes that EffectMaker unifies reasoning and generation for customized visual effect cloning, while EffectData supports broader research and applications. The authors note limitations for highly complex motion and synthetic-data realism.

  • Conclusion: EffectMaker integrates MLLM reasoning with DiT in-context learning to replicate complex reference effects across diverse scenarios.The framework is presented as a reasoning–generation approach for customized visual effect cloning.
  • Conclusion: EffectData provides systematic captions and a high-quality paired VFX resource for future research and applications in art, gaming, and advertising.The dataset is described as the largest and high-quality paired VFX dataset to date.
  • Limitations: EffectMaker may struggle with extremely complex effects involving rapid or large motions because of the base model’s capacity bound.This is identified as a limitation of the current approach.
  • Limitations: Synthetic training data may introduce bias and may not fully capture noisy, ambiguous, or overlapping real-world VFX.Future work will explore stronger base models and diverse real-world data sources.

A. Additional implementation details

The implementation details specify training, sampling, conditioning, evaluation, and user-study procedures for EffectMaker. The framework uses variable-resolution inputs, semi-logit-normal timestep sampling, DiT-based denoising, and human preference evaluation.

  • Training and sampling: The target video’s shorter side is standardized to 704 pixels, while the longer side is scaled to a multiple of 32 up to 1280 pixels.This multi-resolution strategy supports variable input sizes while limiting distortion and matching the VAE downsampling factor.
  • Training and sampling: Timestep sampling uses a semi-logit-normal distribution that biases samples toward middle timesteps instead of sampling uniformly.The timestep is computed from a standard normal variable and the total number of diffusion timesteps T.
  • Training and sampling: The DiT jointly processes target-image, noise, and reference latents, progressively denoising them under MLLM semantic features with flow-matching sampling.The reported configuration uses the UniPC sampler with 30 denoising steps and classifier-free guidance.
  • Efficiency: MLLM understanding requires 2 seconds, DiT generation takes 2 minutes, and end-to-end inference consumes 48 GB of GPU memory on an H20 GPU.Model sharding, CPU offloading, sequence parallelism, and quantization can further reduce memory use.
  • Evaluation: Video quality is evaluated with Visual Quality, Motion Quality, and Text Alignment, alongside Effect Class Alignment Score and Reference Alignment Score.CAS and RAS use Gemini2.5 to score alignment from 0 to 5 against an effect name or reference frames.
  • User study: The user study included 30 participants answering 28 questions across 14 OpenVFX effect categories, comparing side-by-side results using three criteria.Detailed preference percentages for each class and dimension are reported in Figure 11.

B. Dataset details

EffectData is built through a synthetic pipeline combining diverse subjects, a structured VFX taxonomy, instruction generation, image editing, and video synthesis. The resulting expansion strategy supports large-scale paired VFX data generation across atmospheric and transformation effects.

  • Pipeline overview: The synthetic VFX pipeline is designed to overcome the limited scale and diversity of existing VFX datasets through large-scale paired-data generation.It consists of five major steps for producing supervised VFX learning data.
  • Subject collection: The subject pool emphasizes human portraits and animals, with preprocessing for resolution, text removal, and image-quality filtering.The filtering removes multi-person images and samples where the primary subject occupies too little of the image.
  • VFX taxonomy: The taxonomy divides effects into atmospheric and transformation categories, covering supernatural overlays and substantial changes to subject appearance.Transformation effects include outfit, identity, and physical transformations.
  • VFX taxonomy: Atmospheric effects are organized by effect elements, geometric patterns, and attachment regions, whose combinations form a broad effect space.Examples include fire, ice, and light; particles, waves, and rings; and regions such as the face, arms, or full body.
  • Instruction generation: An LLM combines atmospheric effect elements, geometric patterns, and attachment regions into diverse editing instructions, while also expanding transformation descriptions.These instructions guide image editing models to integrate effects while preserving the character’s structure.
  • Subject editing: Image editing models apply each instruction to multiple subjects, producing consistent VFX patterns across different subject images.The pipeline experiments with Flux-Kontext, Qwen-Image-Edit, and NanoBanana.
  • Video generation: Paired source and target images are converted into videos by generating transition descriptions and using first–last-frame-to-video models.Jimeng3.0 and Wan-FLF2V-14B are cited as example video-generation models.
  • Scalable data generation: A fine-tuned instruction-based VFX model expands a moderate amount of high-quality synthetic data into large-scale data without repeatedly invoking closed-source models.The model is trained on an image-to-video backbone and generates consistent effects across subjects from the same instruction.

C. Additional comparison

Additional visual comparisons with related baselines on the OpenVFX dataset are provided in Figure 15, with dynamic results available on the project page.

  • Additional comparison: Figure 15 provides additional visual comparisons between EffectMaker and related baselines on the OpenVFX dataset.The project page contains dynamic results for these comparisons.

D. Additional visual results

Additional VFX visual results are shown across Higgsfield and EffectData, extending the reported examples beyond OpenVFX.

  • Additional visual results: Figures 16–20 present additional VFX results from Higgsfield and EffectData beyond the OpenVFX examples.Dynamic results are available on the project page.

E. Real-world VFX adaptation.

Despite training only on synthetic VFX data, EffectMaker successfully generalizes zero-shot to real-world VFX transfer, although a synthetic-to-real gap remains in fine-grained details.

  • EffectMaker achieves successful zero-shot generalization to real-world VFX transfer despite training exclusively on synthetic data.The authors manually collected real-world VFX movie clips for evaluation.
  • A synthetic-to-real gap remains in the fine-grained details of transferred real-world effects.The authors plan to incorporate real-world VFX data to address this gap.

F. Failure cases

EffectMaker has failure cases involving rapid, large subject motions and semantically incompatible target images, while supplementary figures provide additional comparisons and visual results.

  • Failure cases: Rapid or large-magnitude subject motions can degrade subject fidelity because of the base model’s limited capacity.A sudden upward take-off while the reference effect is flying can cause fidelity failures.
  • Failure cases: Semantically incompatible first-frame images can produce incoherent effects that fail to express the intended VFX semantics.The paper illustrates this with a sunflower-blooming reference effect applied to a dog image.
  • Failure cases: Proper user interactions can avoid failures caused by semantic mismatch between the target image and reference effect.
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