TY - JOUR
T1 - SHREC 2024
T2 - Recognition of dynamic hand motions molding clay
AU - Veldhuijzen, Ben
AU - Veltkamp, Remco C.
AU - Ikne, Omar
AU - Allaert, Benjamin
AU - Wannous, Hazem
AU - Emporio, Marco
AU - Giachetti, Andrea
AU - LaViola, Joseph J.
AU - He, Ruiwen
AU - Benhabiles, Halim
AU - Cabani, Adnane
AU - Fleury, Anthony
AU - Hammoudi, Karim
AU - Gavalas, Konstantinos
AU - Vlachos, Christoforos
AU - Papanikolaou, Athanasios
AU - Romanelis, Ioannis
AU - Fotis, Vlassis
AU - Arvanitis, Gerasimos
AU - Moustakas, Konstantinos
AU - Hanik, Martin
AU - Nava-Yazdani, Esfandiar
AU - von Tycowicz, Christoph
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/10
Y1 - 2024/10
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.
AB - 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.
KW - 3D shape retrieval challenge
KW - Gesture recognition
KW - Hand skeleton gestures
KW - Motion capture
KW - Neural networks
KW - SHREC
UR - http://www.scopus.com/inward/record.url?scp=85199347537&partnerID=8YFLogxK
U2 - 10.1016/j.cag.2024.104012
DO - 10.1016/j.cag.2024.104012
M3 - Article
AN - SCOPUS:85199347537
SN - 0097-8493
VL - 123
JO - Computers and Graphics (Pergamon)
JF - Computers and Graphics (Pergamon)
M1 - 104012
ER -