EffectMaker：统一推理与生成以实现定制化视觉效果创建
EffectMaker: Unifying Reasoning and Generation for Customized Visual Effect Creation
AI总结：
EffectMaker通过统一推理生成框架实现定制化视觉效果创建，利用多模态模型和扩散变换器提升效果质量和一致性，构建大规模数据集增强泛化能力。
摘要：
视觉效果（VFX）对于增强视频内容的表达力和创造力至关重要，但高质量效果的制作通常需要专业知识和昂贵的生产流程。现有的AIGC系统在VFX生成方面面临重大挑战，因为效果特定数据稀缺，且建模超自然或风格化效果具有固有难度。此外，这些方法往往需要每个效果的微调，严重限制了其可扩展性和对新VFX的泛化能力。在本工作中，我们提出了EffectMaker，一种统一的推理生成框架，能够实现基于参考的VFX定制。EffectMaker采用多模态大语言模型来解读高级效果语义并推理解释如何适应目标主体，同时扩散变换器利用上下文学习捕捉参考视频中的细粒度视觉线索。这两个组件形成了一种语义-视觉双路径指导机制，能够实现准确、可控且效果一致的合成，而无需每个效果的微调。此外，我们构建了EffectData，最大的高质量合成数据集，包含13万视频，覆盖3000个VFX类别，以提高泛化能力和可扩展性。实验表明，EffectMaker在视觉质量和效果一致性方面优于最先进的基线，提供了一种可扩展且灵活的定制VFX生成范式。项目页面：https://effectmaker.github.io
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
