EffectMaker: Unifying Reasoning and Generation for Customized Visual Effect Creation
- URL: http://arxiv.org/abs/2603.06014v1
- Date: Fri, 06 Mar 2026 08:09:14 GMT
- Title: EffectMaker: Unifying Reasoning and Generation for Customized Visual Effect Creation
- Abstract summary: EffectMaker is a unified reasoning-generation framework that enables reference-based VFX customization.<n>We construct EffectData, the largest high-quality synthetic dataset containing 130k videos across 3k VFX categories.<n>Experiments show EffectMaker achieves superior visual quality and effect consistency over state-of-the-art baselines.
- Score: 27.31323449481923
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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
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