TY - JOUR A1 - Caputo, Ariel A1 - Emporio, Marco A1 - Giachetti, Andrea A1 - Cristani, Marco A1 - Borghi, Guido A1 - D'Eusanio, Andrea A1 - Le, Minh-Quan A1 - Nguyen, Hai-Dang A1 - Tran, Minh-Triet A1 - Ambellan, Felix A1 - Hanik, Martin A1 - Navayazdani, Esfandiar A1 - Tycowicz, Christoph von T1 - SHREC 2022 Track on Online Detection of Heterogeneous Gestures JF - Computers and Graphics N2 - This paper presents the outcomes of a contest organized to evaluate methods for the online recognition of heterogeneous gestures from sequences of 3D hand poses. The task is the detection of gestures belonging to a dictionary of 16 classes characterized by different pose and motion features. The dataset features continuous sequences of hand tracking data where the gestures are interleaved with non-significant motions. The data have been captured using the Hololens 2 finger tracking system in a realistic use-case of mixed reality interaction. The evaluation is based not only on the detection performances but also on the latency and the false positives, making it possible to understand the feasibility of practical interaction tools based on the algorithms proposed. The outcomes of the contest's evaluation demonstrate the necessity of further research to reduce recognition errors, while the computational cost of the algorithms proposed is sufficiently low. Y1 - 2022 U6 - https://doi.org/10.1016/j.cag.2022.07.015 VL - 107 SP - 241 EP - 251 ER - TY - JOUR A1 - Veldhuijzen, Ben A1 - Veltkamp, Remco C. A1 - Ikne, Omar A1 - Allaert, Benjamin A1 - Wannous, Hazem A1 - Emporio, Marco A1 - Giachetti, Andrea A1 - LaViola Jr, Joseph J. A1 - He, Ruiwen A1 - Benhabiles, Halim A1 - Cabani, Adnane A1 - Fleury, Anthony A1 - Hammoudi, Karim A1 - Gavalas, Konstantinos A1 - Vlachos, Christoforos A1 - Papanikolaou, Athanasios A1 - Romanelis, Ioannis A1 - Fotis, Vlassis A1 - Arvanitis, Gerasimos A1 - Moustakas, Konstantinos A1 - Hanik, Martin A1 - Nava-Yazdani, Esfandiar A1 - von Tycowicz, Christoph T1 - SHREC 2024: Recognition Of Dynamic Hand Motions Molding Clay JF - Computers & Graphics N2 - Gesture recognition is a tool to enable novel interactions with different techniques and applications, like Mixed Reality and Virtual Reality environments. With all the recent advancements in gesture recognition from skeletal data, it is still unclear how well state-of- the-art techniques perform in a scenario using precise motions with two hands. This paper presents the results of the SHREC 2024 contest organized to evaluate methods for their recognition of highly similar hand motions using the skeletal spatial coordinate data of both hands. The task is the recognition of 7 motion classes given their spatial coordinates in a frame-by-frame motion. The skeletal data has been captured using a Vicon system and pre-processed into a coordinate system using Blender and Vicon Shogun Post. We created a small, novel dataset with a high variety of durations in frames. This paper shows the results of the contest, showing the techniques created by the 5 research groups on this challenging task and comparing them to our baseline method. Y1 - 2024 ER -