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Learning to Paint With Model-based Deep Reinforcement Learning

arXiv 1903.04411 ↗First posted 11 Mar 2019cs.CV cs.AIcited by 70

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Abstract (arXiv)

We show how to teach machines to paint like human painters, who can use a small number of strokes to create fantastic paintings. By employing a neural renderer in model-based Deep Reinforcement Learning (DRL), our agents learn to determine the position and color of each stroke and make long-term plans to decompose texture-rich images into strokes. Experiments demonstrate that excellent visual effects can be achieved using hundreds of strokes. The training process does not require the experience of human painters or stroke tracking data. The code is available at https://github.com/hzwer/ICCV2019-LearningToPaint.

Key facts. Cited by 70 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Mar 2019.

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Citing papers per year: 2021: 16; 2022: 16; 2023: 14; 2024: 7; 2025: 9; 2026: 8.

70 papers.

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  1. 2021Im2Vec: Synthesizing Vector Graphics without Vector Supervisioncited by 114
  2. 2022Learning to generate line drawings that convey geometry and semanticscited by 82
  3. 2021Paint Transformer: Feed Forward Neural Painting with Stroke Predictioncited by 61
  4. 2021Procedure Planning in Instructional Videos via Contextual Modeling and Model-based Policy Learningcited by 53
  5. 2023Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Methodcited by 48
  6. 2021Rethinking Style Transfer: From Pixels to Parameterized Brushstrokescited by 42
  7. 2021Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Conceptscited by 40
  8. 2021Torch.fx: Practical Program Capture and Transformation for Deep Learning in Pythoncited by 38
  9. 2024ProcessPainter: Learn Painting Process from Sequence Datacited by 37
  10. 2022High-Fidelity Guided Image Synthesis with Latent Diffusion Modelscited by 36
  11. 2022CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector Graphicscited by 34
  12. 2022StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translationcited by 28
38 more
  1. 2021Position, Padding and Predictions: A Deeper Look at Position Information in CNNscited by 26
  2. 2021Creativity and Machine Learning: A Surveycited by 21
  3. 2021Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learningcited by 19
  4. 2021Emergent Graphical Conventions in a Visual Communication Gamecited by 16
  5. 2021DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learningcited by 14
  6. 2023Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Regioncited by 14
  7. 2023Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challengescited by 10
  8. 2022Toward Modeling Creative Processes for Algorithmic Paintingcited by 9
  9. 2022Im2Oil: Stroke-Based Oil Painting Rendering with Linearly Controllable Fineness Via Adaptive Samplingcited by 9
  10. 2024PICASSO: A Feed-Forward Framework for Parametric Inference of CAD Sketches via Rendering Self-Supervisioncited by 9
  11. 2021ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Controlcited by 8
  12. 2022Collaborative Neural Rendering using Anime Character Sheetscited by 8
  13. 2023Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrationscited by 8
  14. 2021Intelli-Paint: Towards Developing Human-like Painting Agentscited by 6
  15. 2022Paint2Pix: Interactive Painting based Progressive Image Synthesis and Editingcited by 6
  16. 2024Inverse Painting: Reconstructing The Painting Processcited by 6
  17. 2022PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Paintingcited by 5
  18. 2023Learning Geometry-aware Representations by Sketchingcited by 5
  19. 2022RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generationcited by 4
  20. 2022Stroke-based Rendering: From Heuristics to Deep Learningcited by 4
  21. 2023Computational Approaches for Traditional Chinese Painting: From the "Six Principles of Painting" Perspectivecited by 3
  22. 2023Emergent Communication in Interactive Sketch Question Answeringcited by 3
  23. 2023Segmentation-Based Parametric Paintingcited by 3
  24. 2025MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Cloudscited by 3
  25. 2021MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement Learningcited by 2
  26. 2022From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching Agentcited by 2
  27. 2022Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learningcited by 2
  28. 2023TorchBench: Benchmarking PyTorch with High API Surface Coveragecited by 2
  29. 2021Using Python for Model Inference in Deep Learningcited by 1
  30. 2021Graph Convolutional Memory using Topological Priorscited by 1
  31. 2022Multi-granularity Association Learning Framework for on-the-fly Fine-Grained Sketch-based Image Retrievalcited by 1
  32. 2023Interactive Neural Paintingcited by 1
  33. 2023Collaborative Neural Paintingcited by 1
  34. 2024depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researcherscited by 1
  35. 2024CalliRewrite: Recovering Handwriting Behaviors from Calligraphy Images without Supervisioncited by 1
  36. 2024Hyperstroke: A Novel High-quality Stroke Representation for Assistive Artistic Drawingcited by 1
  37. 2024Level of agreement between emotions generated by Artificial Intelligence and human evaluation: a methodological proposalcited by 1
  38. 2025Sketch & Paint: Stroke-by-Stroke Evolution of Visual Artworkscited by 1

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