SniffyArt

Smell gestures play a crucial role in the investigation of past smells in the visual arts yet their automated recognition poses significant challenges. We introduce the SniffyArt dataset, consisting of 1941 individuals represented in 441 historical artworks. Each person is annotated with a tightly fitting bounding box, 17 pose keypoints, and a gesture label. By integrating these annotations, the dataset enables the development of hybrid classification approaches for smell gesture recognition. The dataset’s high-quality human pose estimation keypoints are achieved through the merging of five separate sets of keypoint annotations per person. The SniffyArt dataset lays a solid foundation for future research and the exploration of multi-task approaches leveraging pose keypoints and person boxes to advance human gesture and olfactory dimension analysis in historical artworks.