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.

翻译：嗅覺手勢在視覺藝術中研究歷史氣味時扮演關鍵角色，然而其自動識別仍面臨重大挑戰。本文介紹SniffyArt數據集，包含441幅歷史藝術作品中描繪的1941個人物。每個人物以緊密貼合的邊界框、17個姿態關鍵點及一個手勢標籤進行標註。通過整合這些標註，該數據集得以開發嗅覺手勢識別的混合分類方法。數據集的高質量人體姿態估計關鍵點是通過合併每個人物的五組獨立關鍵點標註實現。本文亦提供基線分析，評估代表性算法在檢測、關鍵點估計及分類任務上的表現，展現關鍵點估計與嗅覺手勢分類相結合的潛力。SniffyArt數據集為未來研究及利用姿態關鍵點與人物框推進歷史藝術作品中人類手勢及嗅覺維度分析的多任務方法探索奠定了堅實基礎。