Title: Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation Under Hand-Object Interaction
Authors: Armagan, Anil
Garcia-Hernando, Guillermo
Baek, Seungryul
Hampali, Shreyas
Rad, Mahdi
Zhang, Zhaohui
Xie, Shipeng
Chen, MingXiu
Zhang, Boshen
Xiong, Fu
Yang, Xiao
Cao, Zhiguo
Yuan, Junsong
Ren, Pengfei
Huang, Weiting
Sun, Haifeng
Hrúz, Marek
Kanis, Jakub
Krňoul, Zdeněk
Wan, Qingfu
Li, Shile
Yang, Linlin
Lee, Dongheui
Yao, Angela
Zhou, Weiguo
Mei, Sijia
Liu, Yunhui
Spurr, Adrian
Iqbal, Umar
Molchanov, Pavlo
Weinzaepfel, Philippe
Brégier, Romain
Rogez, Grégory
Lepetit, Vincent
Kim, Tae-Kyun
Citation: ARMAGAN, A., GARCIA-HERNANDO, G., BAEK, S., HAMPALI, S., RAD, M., ZHANG, Z., XIE, S., CHEN, M., ZHANG, B., XIONG, F., YANG, X., CAO, Z., YUAN, J., REN, P., HUANG, W., SUN, H., HRÚZ, M., KANIS, J., KRŇOUL, Z., WAN, Q., LI, S., YANG, L., LEE, D., YAO, A., ZHOU, W., MEI, S., LIU, Y., SPURR, A., IQBAL, U., MOLCHANOV, P., WEINZAEPFEL, P., BRÉGIER, R., ROGEZ, G., LEPETIT, V., KIM, T. Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation Under Hand-Object Interaction. In: Computer Vision - ECCV 2020, 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII. Cham: Springer, 2020. s. 85-101. ISBN 978-3-030-58591-4, ISSN 0302-9743.
Issue Date: 2020
Publisher: Springer
Document type: konferenční příspěvek
URI: 2-s2.0-85097407772
ISBN: 978-3-030-58591-4
ISSN: 0302-9743
Keywords in different language: Hand Pose Estiamtion
Abstract in different language: We study how well different types of approaches generalise in the task of 3D hand pose estimation under single hand scenarios and hand-object interaction. We show that the accuracy of state-of-the-art methods can drop, and that they fail mostly on poses absent from the training set. Unfortunately, since the space of hand poses is highly dimensional, it is inherently not feasible to cover the whole space densely, despite recent efforts in collecting large-scale training datasets. This sampling problem is even more severe when hands are interacting with objects and/or inputs are RGB rather than depth images, as RGB images also vary with lighting conditions and colors. To address these issues, we designed a public challenge (HANDS’19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set. More exactly, HANDS’19 is designed (a) to evaluate the influence of both depth and color modalities on 3D hand pose estimation, under the presence or absence of objects; (b) to assess the generalisation abilities w.r.t. four main axes: shapes, articulations, viewpoints, and objects; (c) to explore the use of a synthetic hand models to fill the gaps of current datasets. Through the challenge, the overall accuracy has dramatically improved over the baseline, especially on extrapolation tasks, from 27 mm to 13 mm mean joint error. Our analyses highlight the impacts of: Data pre-processing, ensemble approaches, the use of a parametric 3D hand model (MANO), and different HPE methods/backbones.
Rights: Plný text není přístupný.
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Konferenční příspěvky / Conference Papers (KKY)

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