  Smell gestures play a crucial role in the investigation of past smells in the
visual arts yet their automated recognition poses significant challenges. This
paper introduces 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 datasets high-quality human pose
estimation keypoints are achieved through the merging of five separate sets of
keypoint annotations per person. The paper also presents a baseline analysis,
evaluating the performance of representative algorithms for detection, keypoint
estimation, and classification tasks, showcasing the potential of combining
keypoint estimation with smell gesture classification. 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.
