Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture

Tang, Danhang; Chang, Hyung Jin; Tejani, Alykhan; Kim, Tae-Kyun

doi:10.1109/cvpr.2014.490

Cited by 398Hand Gesture Recognition SystemsHuman Pose and Action RecognitionRobot Manipulation and Learning

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Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture

Tang, Danhang, Chang, Hyung Jin, Tejani, Alykhan, Kim, Tae-Kyun·2014·2014 IEEE Conference on Computer Vision and Pattern Recognition

DOI 10.1109/cvpr.2014.490

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Abstract

In this paper we present the Latent Regression Forest (LRF), a novel framework for real-time, 3D hand pose estimation from a single depth image. In contrast to prior forest-based methods, which take dense pixels as input, classify them independently and then estimate joint positions afterwards, our method can be considered as a structured coarse-to-fine search, starting from the centre of mass of a point cloud until locating all the skeletal joints. The searching process is guided by a learnt Latent Tree Model which reflects the hierarchical topology of the hand. Our main contributions can be summarised as follows: (i) Learning the topology of the hand in an unsupervised, data-driven manner. (ii) A new forest-based, discriminative framework for structured search in images, as well as an error regression step to avoid error accumulation. (iii) A new multi-view hand pose dataset containing 180K annotated images from 10 different subjects. Our experiments show that the LRF out-performs state-of-the-art methods in both accuracy and efficiency.

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Authors: Tang, D., Chang, H. J., Tejani, A., & Kim, T.-K.
Affiliations: Imperial College London (GB)
ORCID: Tang, Danhang
Chang, Hyung Jin
Kim, Tae-Kyun
Venue: 2014 IEEE Conference on Computer Vision and Pattern Recognition
Publisher: IEEE
Published: 2014-06
Pages: 3786-3793
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr.2014.490
OpenAlex: W2093414253
Cited by: 398
References: 20
Citations by year: 2026: 13 · 2025: 18 · 2024: 17 · 2023: 26 · 2022: 19 · 2021: 47 · 2020: 42 · 2019: 44 · 2018: 54 · 2017: 47 · 2016: 37 · 2015: 31 · 2014: 3
FWCI: 30.33
Type: conference paper
Language: en
Open access: closed
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
Funders: Samsung Advanced Institute of Technology
Engineering and Physical Sciences Research Council (EP/J012106/1)
Topics: Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
Keywords: 3D hand pose estimation, regression forests, latent tree models, structured search, depth images, real-time pose estimation

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Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture

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