Sketch2Anim: Towards Transferring Sketch Storyboards into 3D Animation
Lei Zhong, Chuan Guo, Yiming Xie, Jiawei Wang, Changjian Li
Abstract
Storyboarding is widely used for creating 3D animations. Animators use the 2D sketches in storyboards as references to craft the desired 3D animations through a trial-and-error process. The traditional approach requires exceptional expertise and is both labor-intensive and time-consuming. Consequently, there is a high demand for automated methods that can directly translate 2D storyboard sketches into 3D animations. This task is under-explored to date and inspired by the significant advancements of motion diffusion models, we propose to address it from the perspective of conditional motion synthesis. We thus present Sketch2Anim , composed of two key modules for sketch constraint understanding and motion generation. Specifically, due to the large domain gap between the 2D sketch and 3D motion, instead of directly conditioning on 2D inputs, we design a 3D conditional motion generator that simultaneously leverages 3D keyposes, joint trajectories, and action words, to achieve precise and fine-grained motion control. Then, we invent a neural mapper dedicated to aligning user-provided 2D sketches with their corresponding 3D keyposes and trajectories in a shared embedding space, enabling, for the first time , direct 2D control of motion generation. Our approach successfully transfers storyboards into high-quality 3D motions and inherently supports direct 3D animation editing, thanks to the flexibility of our multi-conditional motion generator. Comprehensive experiments and evaluations, and a user perceptual study demonstrate the effectiveness of our approach. The code, data, trained models, and sketch-based motion designing interface are at https://zhongleilz.github.io/Sketch2Anim/.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext bf2f80de-6e41-4956-9364-6d1aba2feaa2Cited by top-tier papers6
- ProjFlow: Projection Sampling with Flow Matching for Zero‑Shot Exact Spatial Motion ControlAkihisa Watanabe, Qing Yu, Edgar Simo-Serra, Kent FujiwaraCVPR 2026 · 6 citations
- SketchDynamics: Exploring Free-Form Sketches for Dynamic Intent Expression in Animation GenerationBoyu Li, Lin-Ping Yuan, Zeyu Wang, Hongbo FuCHI 2026 · 1 citation
- DancingBox: A Lightweight MoCap System for Character Animation from Physical ProxiesHaocheng Yuan, Adrien Bousseau, Hao Pan, Lei Zhong et al.CHI 2026 · 1 citation
- Notational Animating: An Interactive Approach to Creating and Editing Animation KeyframesXinyu Shi, Li-Yi Wei, Nanxuan Zhao, Jian Zhao et al.CHI 2026 · 1 citation
- Sketch2Colab: Sketch-Conditioned Multi-Human Animation via Controllable Flow DistillationDivyanshu Daiya, Aniket BeraCVPR 2026
Builds on31
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 13,211 citations
- Directly Denoising Diffusion ModelsDan Zhang, Jingjing Wang, Feng LuoICML 2024 · 11,724 citations
- T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion ModelsChong Mou, Xintao Wang, Liangbin Xie, Yanze Wu et al.AAAI 2024 · 1,641 citations
- MotionGPT: Human Motion as a Foreign LanguageBiao Jiang, Xin Chen, Wen Liu, Jingyi Yu et al.NeurIPS 2023 · 698 citations
Related papers
- FlipSketch: Flipping Static Drawings to Text-Guided Sketch AnimationsHmrishav Bandyopadhyay, Yi-Zhe SongCVPR 2025
- Control3D: Towards Controllable Text-to-3D GenerationYang Chen, Yingwei Pan, Yehao Li, Ting Yao et al.ACM MM 2023 · 54 citations
- SketchDream: Sketch-based Text-To-3D Generation and EditingFeng-Lin Liu, Hongbo Fu, Yu-Kun Lai, Lin GaoSIGGRAPH 2024 · 30 citations
- Flexible Motion In-betweening with Diffusion ModelsSetareh Cohan, Guy Tevet, Daniele Reda, Xue Bin Peng et al.SIGGRAPH 2024 · 40 citations
- Stroke3D: Lifting 2D strokes into rigged 3D model via latent diffusion modelsRuisi Zhao, Haoren Zheng, Zongxin Yang, Hehe Fan et al.ICLR 2026 · 2 citations
