SynGallery: A Synthetic Gallery of Real Paintings for Instance-Level Artwork Recognition

Patryk Bartkowiak1, Jakub Markil1, Bartosz Kotrys2, Dominik L. Michels3, Sören Pirk4, Wojtek Palubicki1
1Adam Mickiewicz University, 2ArtiCollect, 3KAUST, 4Kiel University
VISART @ ECCV 2026
arXiv Code Dataset HF Collection
Cavalier and Shepherd rendered at a 150 degree viewing angle
150°
Cavalier and Shepherd rendered at a 120 degree viewing angle
120°
Cavalier and Shepherd rendered at a 90 degree viewing angle
90°
Cavalier and Shepherd rendered at a 60 degree viewing angle
60°
Cavalier and Shepherd rendered at a 30 degree viewing angle
30°

SynGallery places each painting in a procedurally generated 3D gallery and renders it from many viewpoints while preserving its identity. Above: Cavalier and Shepherd by Francesco Casanova, seen from five viewpoints.

Abstract

Instance-level artwork recognition requires matching a handheld visitor photograph to a specific work in a large museum collection. This is difficult because museum datasets typically provide clean catalog images for training, while test queries are captured under oblique viewpoints, gallery lighting, reflections, frames, and other scene-level variations. We present SynGallery, a synthetic gallery dataset for artwork retrieval that addresses this gap without collecting additional visitor photographs. Starting from catalog images of real paintings, we place each artwork into a procedurally generated 3D gallery scene and render it from multiple viewpoints under varied geometric and appearance conditions, while preserving the exact identity of the original work.

The resulting dataset contains 24,490 rendered views of 4,898 paintings from the Met benchmark. We show that these synthetic views provide a stronger training signal than the corresponding studio photographs. At a matched image budget, training only on SynGallery improves closed-world painting recognition from 67.18 to 73.47 GAP−. When added to the full Met training set, SynGallery improves the published benchmark protocol from 35.97 to 38.48 GAP, without changing the retrieval architecture or adding real visitor photos. Ablations show that the gain comes primarily from geometric viewpoint variation rather than low-level photographic realism: simulated blur, sensor noise, and compression consistently reduce performance.

Method Overview

Overview of the SynGallery pipeline: a catalog image of a real painting is placed into a procedurally generated 3D gallery scene and rendered from multiple viewpoints under varied conditions, preserving the painting's identity.

From a single catalog image, SynGallery places each painting in a procedurally generated 3D gallery and renders it from multiple viewpoints under varied geometric and appearance conditions, while preserving the exact identity of the original work.

Sample Renders

Acknowledgments

ArtiCollect

This research was conducted in collaboration with ArtiCollect, who funded the work, contributed to data collection, and provided domain expertise in art.

BibTeX

@misc{bartkowiak2026syngallery,
      title={SynGallery: A Synthetic Gallery of Real Paintings for Instance-Level Artwork Recognition},
      author={Patryk Bartkowiak and Jakub Markil and Bartosz Kotrys and Dominik Michels and Sören Pirk and Wojtek Palubicki},
      year={2026},
      eprint={2607.18907},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2607.18907},
      note={To appear at the VISART VIII Workshop at ECCV 2026},
}