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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.
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.