mlsanityData to 14 Oct 2026

VidSketch: Hand-drawn Sketch-Driven Video Generation with Diffusion Control

arXiv 2502.01101 ↗First posted 3 Feb 2025cs.CV cs.AIcited by 9

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Abstract (arXiv)

With the advancement of generative artificial intelligence, previous studies have achieved the task of generating aesthetic images from hand-drawn sketches, fulfilling the public's needs for drawing. However, these methods are limited to static images and lack the ability to control video animation generation using hand-drawn sketches. To address this gap, we propose VidSketch, the first method capable of generating high-quality video animations directly from any number of hand-drawn sketches and simple text prompts, bridging the divide between ordinary users and professional artists. Specifically, our method introduces a Level-Based Sketch Control Strategy to automatically adjust the guidance strength of sketches during the generation process, accommodating users with varying drawing skills. Furthermore, a TempSpatial Attention mechanism is designed to enhance the spatiotemporal consistency of generated video animations, significantly improving the coherence across frames. You can find more detailed cases on our official website.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 28 of its 34 references that are in the map. First posted on arXiv 3 Feb 2025.

Builds on

28 of 34 references.

References · 28

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  3. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  4. 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,822
  5. 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,464
  6. 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,433
  7. 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Modelscited by 1,351
  8. 2023ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generationcited by 1,045
  9. 2023Video-P2P: Video Editing with Cross-attention Controlcited by 314
  10. 2022Sketch-Guided Text-to-Image Diffusion Modelscited by 180
  11. 2023Rich Human Feedback for Text-to-Image Generationcited by 141
  12. 2023VidToMe: Video Token Merging for Zero-Shot Video Editingcited by 113
16 more
  1. 2024Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesiscited by 105
  2. 2023SimDA: Simple Diffusion Adapter for Efficient Video Generationcited by 101
  3. 2023Generative Image Dynamicscited by 94
  4. 2023InstructVideo: Instructing Video Diffusion Models with Human Feedbackcited by 86
  5. 2023CCEdit: Creative and Controllable Video Editing via Diffusion Modelscited by 82
  6. 2023LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generationcited by 59
  7. 2024ViewDiff: 3D-Consistent Image Generation with Text-to-Image Modelscited by 57
  8. 2023Hierarchical Spatio-temporal Decoupling for Text-to-Video Generationcited by 50
  9. 2024FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept Compositioncited by 49
  10. 2023Breathing Life Into Sketches Using Text-to-Video Priorscited by 48
  11. 2023PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Modelscited by 30
  12. 2024It's All About Your Sketch: Democratising Sketch Control in Diffusion Modelscited by 29
  13. 2023Motion-Conditioned Image Animation for Video Editingcited by 26
  14. 2023BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Modelscited by 20
  15. 2024FlipSketch: Flipping Static Drawings to Text-Guided Sketch Animationscited by 8
  16. 2024Sketch-Guided Scene Image Generationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 4.

9 papers.