# jiupinjia/stylized-neural-painting

Official Pytorch implementation of the preprint paper "Stylized Neural Painting", in CVPR 2021.

Repository: https://github.com/jiupinjia/stylized-neural-painting
Canonical: https://ross.abutalabs.com/products/stylized-neural-painting
Homepage: https://jiupinjia.github.io/neuralpainter/
Language: Python
License: CC0-1.0
License Family: permissive
Topics: style-transfer, painting-translation, stroke-parameters, neural-rendering
Last push: 2021-08-26T21:08:20+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2116, "days_push": 1833, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1600, forks 261 (observed 2026-08-28T04:05:09.556284+00:00)

## What it is
Official PyTorch implementation of the CVPR 2021 paper 'Stylized Neural Painting', which translates photos into vectorized painting artworks by predicting physically meaningful stroke parameters rendered via a learned neural renderer. It includes the full training and inference pipeline, Colab demos, and joint optimization with neural style transfer.

## Use cases
- convert a photo into an oil or watercolor painting
- generate stroke-by-stroke painting animations from images
- reproduce results from the Stylized Neural Painting paper
- apply artistic style transfer to images with controllable painting styles
- train a neural renderer that imitates a vector graphics renderer
- run image-to-painting translation on CPU without a GPU

## When to choose
- you need research-grade image-to-painting translation with vectorized stroke output
- you want to experiment with differentiable neural rendering or stroke parameter optimization
- you want a runnable PyTorch pipeline with pretrained demos and Colab notebooks

## When to avoid
- you need a production-ready, actively maintained style transfer service
- you require commercial usage - the code is under a CC BY-NC-SA non-commercial license
- you need fast real-time image stylization rather than slow optimization-based rendering

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, image-processing, deep-learning
- domain: computer-vision, image-processing, artificial-intelligence, graphics
- platform: python, cross-platform
- tags: style-transfer, neural-rendering, image-to-painting, stroke-parameters, pytorch, cvpr-2021, research-code, gpu

## Member repositories
- jiupinjia/stylized-neural-painting (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:09.556284+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:53:11.793969+00:00, confidence not recorded.
  - readme: https://github.com/jiupinjia/stylized-neural-painting (fetched 2026-08-28T04:05:09.556284+00:00, sha 2cefbf2486d1)
  - homepage: https://jiupinjia.github.io/neuralpainter/ (fetched 2026-08-29T11:24:25.690982+00:00, sha d9af3d5f3296)
- Data as of 2026-08-30T08:39:29.467469+00:00.