标题： FairyGen：从单个儿童绘制的角色生成故事卡通视频 显示英文标题

标题： FairyGen: Storied Cartoon Video from a Single Child-Drawn Character

摘要： 我们提出FairyGen，一个自动系统，可以从单个儿童的绘画中生成以故事驱动的卡通视频，同时忠实地保留其独特的艺术风格。 与以往主要关注角色一致性和基本动作的讲故事方法不同，FairyGen明确地将角色建模与风格化背景生成分离，并结合电影镜头设计以支持富有表现力和连贯的故事讲述。 给定一个单独的角色草图，我们首先使用MLLM生成一个结构化的分镜脚本，其中包含指定环境设置、角色动作和摄像机视角的镜头级描述。 为了确保视觉一致性，我们引入了一个风格传播适配器，该适配器捕捉角色的视觉风格并将其应用于背景，忠实地保留角色的完整视觉身份，同时合成风格一致的场景。 一个镜头设计模块通过基于分镜脚本的帧裁剪和多视角合成进一步增强视觉多样性和电影质量。 为了动画化故事，我们重建角色的3D代理以推导出物理上合理的运动序列，然后用于微调基于MMDiT的图像到视频扩散模型。 我们进一步提出一个两阶段的动作定制适配器：第一阶段从时间无序的帧中学习外观特征，将身份与动作分离；第二阶段使用冻结身份权重的时间步移位策略来建模时间动态。 一旦训练完成，FairyGen可以直接渲染与分镜脚本对齐的多样化且连贯的视频场景。 大量实验表明，我们的系统生成的动画在风格上忠实，叙事结构自然运动，突显了其在个性化和引人入胜的故事动画方面的潜力。 代码将在https://github.com/GVCLab/FairyGen提供。 显示英文摘要

摘要： We propose FairyGen, an automatic system for generating story-driven cartoon videos from a single child's drawing, while faithfully preserving its unique artistic style. Unlike previous storytelling methods that primarily focus on character consistency and basic motion, FairyGen explicitly disentangles character modeling from stylized background generation and incorporates cinematic shot design to support expressive and coherent storytelling. Given a single character sketch, we first employ an MLLM to generate a structured storyboard with shot-level descriptions that specify environment settings, character actions, and camera perspectives. To ensure visual consistency, we introduce a style propagation adapter that captures the character's visual style and applies it to the background, faithfully retaining the character's full visual identity while synthesizing style-consistent scenes. A shot design module further enhances visual diversity and cinematic quality through frame cropping and multi-view synthesis based on the storyboard. To animate the story, we reconstruct a 3D proxy of the character to derive physically plausible motion sequences, which are then used to fine-tune an MMDiT-based image-to-video diffusion model. We further propose a two-stage motion customization adapter: the first stage learns appearance features from temporally unordered frames, disentangling identity from motion; the second stage models temporal dynamics using a timestep-shift strategy with frozen identity weights. Once trained, FairyGen directly renders diverse and coherent video scenes aligned with the storyboard. Extensive experiments demonstrate that our system produces animations that are stylistically faithful, narratively structured natural motion, highlighting its potential for personalized and engaging story animation. The code will be available at https://github.com/GVCLab/FairyGen