# Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation

arXiv: https://arxiv.org/abs/1811.10666 · HTML: https://mlsanity.com/paper/1811.10666/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2018.

## Built on by (most cited first, 17 of 17)

- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity](https://mlsanity.com/paper/2212.10431/) (2022)
- [Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey](https://mlsanity.com/paper/2105.12694/) (2021)
- [Semantics-aware Multi-modal Domain Translation:From LiDAR Point Clouds to Panoramic Color Images](https://mlsanity.com/paper/2106.13974/) (2021)
- [Learning to Generate Poetic Chinese Landscape Painting with Calligraphy](https://mlsanity.com/paper/2305.04719/) (2023)
- [Atom-Level Optical Chemical Structure Recognition with Limited Supervision](https://mlsanity.com/paper/2404.01743/) (2024)
- [Multi-Domain Image-to-Image Translation with Adaptive Inference Graph](https://mlsanity.com/paper/2101.03806/) (2021)
- [A deep learning approach to clustering visual arts](https://mlsanity.com/paper/2106.06234/) (2021)
- [Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection](https://mlsanity.com/paper/2303.05148/) (2023)
- [Interactive Image Inpainting Using Semantic Guidance](https://mlsanity.com/paper/2201.10753/) (2022)
- [Depth- and Semantics-aware Multi-modal Domain Translation: Generating 3D Panoramic Color Images from LiDAR Point Clouds](https://mlsanity.com/paper/2302.07661/) (2023)
- [A Survey on Adversarial Image Synthesis](https://mlsanity.com/paper/2106.16056/) (2021)
- [Style Transfer with Target Feature Palette and Attention Coloring](https://mlsanity.com/paper/2111.04028/) (2021)
- [A Unified Architecture of Semantic Segmentation and Hierarchical Generative Adversarial Networks for Expression Manipulation](https://mlsanity.com/paper/2112.04603/) (2021)
- [Semantically-aware Mask CycleGAN for Translating Artistic Portraits to Photo-realistic Visualizations](https://mlsanity.com/paper/2306.06577/) (2023)
- [Scene Depth Estimation from Traditional Oriental Landscape Paintings](https://mlsanity.com/paper/2403.03408/) (2024)
- [Domain-adaptive Zero-Shot Image Enhancement via Locality-Constrained Diffusion Guidance](https://mlsanity.com/paper/2609.35289/) (2026)

## Abstract (arXiv)

The applicability of computer vision to real paintings and artworks has been rarely investigated, even though a vast heritage would greatly benefit from techniques which can understand and process data from the artistic domain. This is partially due to the small amount of annotated artistic data, which is not even comparable to that of natural images captured by cameras. In this paper, we propose a semantic-aware architecture which can translate artworks to photo-realistic visualizations, thus reducing the gap between visual features of artistic and realistic data. Our architecture can generate natural images by retrieving and learning details from real photos through a similarity matching strategy which leverages a weakly-supervised semantic understanding of the scene. Experimental results show that the proposed technique leads to increased realism and to a reduction in domain shift, which improves the performance of pre-trained architectures for classification, detection, and segmentation. Code is publicly available at: https://github.com/aimagelab/art2real.