hzwer/ICCV2019-LearningToPaint

Health v2 · maintenance only

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2305 stars · 311 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch research implementation of the ICCV 2019 paper 'Learning to Paint With Model-based Deep Reinforcement Learning'. It trains agents that decompose images into a small number of painterly strokes using a neural renderer, without human painting data.

Use cases

• convert an image into a sequence of brush strokes
• generate a video of a machine painting a picture
• research on model-based deep reinforcement learning for image generation
• train a neural renderer for stroke-based image synthesis
• reproduce ICCV 2019 learning-to-paint experiments

When to choose

• you want to reproduce or extend the LearningToPaint paper
• you need stroke-based image stylization or painting-process animation
• you study model-based DRL with neural renderers

When to avoid

• you need a production-ready image editing tool
• you want modern maintained dependencies (it pins PyTorch 1.1.0)
• you need high-fidelity photorealistic image generation

Facets

library · maturity maintenance

machine-learning deep-learning reinforcement-learning computer-vision image-processing deep-learning reinforcement-learning computer-vision image-processing artificial-intelligence python pytorch neural-renderer stroke-painting research-code iccv2019 linux gpu

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markdown · JSON · MCP: product_card(name="hzwer/ICCV2019-LearningToPaint")