# Re:Draw -- Context Aware Translation as a Controllable Method for Artistic Production

arXiv: https://arxiv.org/abs/2401.03499 · HTML: https://mlsanity.com/paper/2401.03499/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 7 Jan 2024.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Few-Shot Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1905.01723/) (2019)
- [Few-shot Image Generation via Cross-domain Correspondence](https://mlsanity.com/paper/2104.06820/) (2021)
- [U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation](https://mlsanity.com/paper/1907.10830/) (2019)
- [Breaking the cycle -- Colleagues are all you need](https://mlsanity.com/paper/1911.10538/) (2019)
- [COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder](https://mlsanity.com/paper/2007.07431/) (2020)
- [Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss](https://mlsanity.com/paper/1908.05840/) (2019)
- [User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks](https://mlsanity.com/paper/1808.03240/) (2018)
- [Towards the Automatic Anime Characters Creation with Generative Adversarial Networks](https://mlsanity.com/paper/1708.05509/) (2017)
- [AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation](https://mlsanity.com/paper/2102.12593/) (2021)
- [Deep learning and face recognition: the state of the art](https://mlsanity.com/paper/1902.03524/) (2019)
- [A survey of comics research in computer science](https://mlsanity.com/paper/1804.05490/) (2018)
- [MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing](https://mlsanity.com/paper/2004.10634/) (2020)
- [PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain](https://mlsanity.com/paper/1903.10146/) (2019)
- [CAST: Character labeling in Animation using Self-supervision by Tracking](https://mlsanity.com/paper/2201.07619/) (2022)
- [Painting Style-Aware Manga Colorization Based on Generative Adversarial Networks](https://mlsanity.com/paper/2107.07943/) (2021)

## Built on by (most cited first, 3 of 3)

- [Sakuga-42M Dataset: Scaling Up Cartoon Research](https://mlsanity.com/paper/2405.07425/) (2024)
- [Generative AI for Cel-Animation: A Survey](https://mlsanity.com/paper/2501.06250/) (2025)
- [ExpressEdit: Fast Editing of Stylized Facial Expressions with Diffusion Models in Photoshop](https://mlsanity.com/paper/2604.03448/) (2026)

## Abstract (arXiv)

We introduce context-aware translation, a novel method that combines the benefits of inpainting and image-to-image translation, respecting simultaneously the original input and contextual relevance -- where existing methods fall short. By doing so, our method opens new avenues for the controllable use of AI within artistic creation, from animation to digital art. As an use case, we apply our method to redraw any hand-drawn animated character eyes based on any design specifications - eyes serve as a focal point that captures viewer attention and conveys a range of emotions, however, the labor-intensive nature of traditional animation often leads to compromises in the complexity and consistency of eye design. Furthermore, we remove the need for production data for training and introduce a new character recognition method that surpasses existing work by not requiring fine-tuning to specific productions. This proposed use case could help maintain consistency throughout production and unlock bolder and more detailed design choices without the production cost drawbacks. A user study shows context-aware translation is preferred over existing work 95.16% of the time.