3D Human Pose Estimation Using Egocentric Depth Data

被引：0

作者：

Baek, Seongmin ^{[1
]}

Gil, Youn-Hee ^{[1
]}

Kim, Yejin ^{[2
]}

机构：

[1] Elect & Telecommun Res Inst, Daejeon, South Korea

[2] Hongik Univ, Sch Games, Sejong, South Korea

来源：

30TH ACM SYMPOSIUM ON VIRTUAL REALITY SOFTWARE AND TECHNOLOGY, VRST 2024 | 2024年

关键词：

Pose estimation; egocentric view; depth data; skeletal joints;

D O I：

10.1145/3641825.3689515

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we present a novel approach for 3D human pose estimation using depth data from egocentric viewpoints. Depth data has the advantage that it is less sensitive to color and lighting changes. We acquired depth data streamed from multiple depth cameras attached to a user's head and calibrated them into a depth map. For joint detection, a ResNet-based network was optimized with the skeletal joints of a Kinect camera. Unlike previous approaches, the proposed approach can track 3D human poses in an egocentric setup with a small dataset.

引用

页数：2

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