FairyGen: Storied Cartoon Video from a Single Child-Drawn Character
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Abstract (arXiv)
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
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 43 of its 60 references that are in the map. First posted on arXiv 26 Jun 2025.
Builds on
43 of 60 references.
References · 43
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31 more
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Built on by
Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2026MuSS: A Large-Scale Dataset and Cinematic Narrative Benchmark for Multi-Shot Subject-to-Video Generationcited by 9
- 2026StoryBlender: Inter-Shot Consistent and Editable 3D Storyboard with Spatial-temporal Dynamicscited by 3
- 2026Sketch2Motion: Text-driven 2D Sketch to 3D Animation via Diffusion-guided Skeleton Optimizationcited by 1
- 2025ASemConsist: Adaptive Semantic Feature Control for Training-Free Identity-Consistent Generationcited by 0
- 2026Sketch2Motion: Text-driven 2D Sketch to 3D Animation via Diffusion-guided Skeleton Optimization27 May 2026
- 2026MuSS: A Large-Scale Dataset and Cinematic Narrative Benchmark for Multi-Shot Subject-to-Video Generation26 Apr 2026
- 2026StoryBlender: Inter-Shot Consistent and Editable 3D Storyboard with Spatial-temporal Dynamics1 Apr 2026
- 2025ASemConsist: Adaptive Semantic Feature Control for Training-Free Identity-Consistent Generation29 Dec 2025
Similar, not cited
Close in wording, no citation either way
- 2026DreamShot: Personalized Storyboard Synthesis with Video Diffusion Prior
- 2023Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation
- 2023Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion Models
- 2026AnimeAgent: Is the Multi-Agent via Image-to-Video models a Good Disney Storytelling Artist?
- 2024CharacterGen: Efficient 3D Character Generation from Single Images with Multi-View Pose Canonicalization
- 2025Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions
- 2025AnimeShooter: A Multi-Shot Animation Dataset for Reference-Guided Video Generation
- 2024DreamRunner: Fine-Grained Compositional Story-to-Video Generation with Retrieval-Augmented Motion Adaptation