Hands represent an important aspect of pictorial narration but have rarely been addressed as an object of study in art history and digital humanities. Although hand gestures play a significant role in conveying emotions, narratives, and cultural symbolism in the context of visual art, a comprehensive terminology for the classification of depicted hand poses is still lacking. In this article, we present the process of creating a new annotated dataset of pictorial hand poses. The dataset is based on a collection of European early modern paintings, from which hands are extracted using human pose estimation (HPE) methods. The hand images are then manually annotated based on art historical categorization schemes. From this categorization, we introduce a new classification task and perform a series of experiments using different types of features, including our newly introduced 2D hand keypoint features, as well as existing neural network-based features. This classification task represents a new and complex challenge due to the subtle and contextually dependent differences between depicted hands. The presented computational approach to hand pose recognition in paintings represents an initial attempt to tackle this challenge, which could potentially advance the use of HPE methods on paintings, as well as foster new research on the understanding of hand gestures in art.

Hands are an important tool for our daily communication with our peers and the world. They allow us to convey information through particular gestures that are either the product of social conventions or personal expressions. Thanks to the sophistication of sensing and computer vision technologies over the past decade, automated hand recognition can now be more easily used and integrated in simple web applications. In a context of digital artworks collections, it means that gestures can now be envisioned as a new browsing tool that goes beyond simple movements to navigate through a 3D digital space. The paper presents Gestures for Artwork Browsing (GAB), a web application which proposes to use hand motions as a way to directly query pictorial hand gestures from the past. Based on materials from a digitized collection of Renaissance paintings, GAB enables users to record a sequence with the hand movement of their choice and outputs an animation reproducing that same sequence with painted hands. Fostering new research possibilities, the project is a novelty in terms of art database browsing and human-computer interaction, as it does not require traditional search tools such as text-based inputs based on metadata, and allows a direct communication with the content of the artworks.

Recent technological advancements in generative Artificial Intelligence (AI) models combined with Augmented Reality (AR) systems represent a new opportunit

In this article, we present a new methodology and visualization system for Italian early modern painted hands. The goal is to outline potential gestural systems over a large corpus of study, in opposition to many art historical works focusing on a single hand gesture or on a specific painter. Furthermore, as most of the actual research in art history is dedicated to symbolic hands, the global approach aims to shed light on less documented hands, such as functional gestures. We first perform unsupervised clustering on a collection of hands, before combining them with classes for symbolic gestures and creating a system of recurrent hand combinations according to their corresponding iconographies. We then introduce an interactive visualization with a network graph, where the user can select an iconography and see the type of hands used in the specific narrative context. Through this experiment, we provide an analysis of the most common types of hands found in our collection, as well as the way they can be associated for particular iconographies. We finally discuss the impact of machine learning models and subjective choices induced on the results at each step, as well as implications of the study of the pictorial detail at large scale.