computer-vision-2

Unsupervised 3D Human Pose Estimation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in unsupervised-3d-human-pose-estimation-2

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Best Model

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Human3.6M

MPI-INF-3DHP

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Use these libraries to find unsupervised-3d-human-pose-estimation-2 models and implementations

Datasets

Human3.6M

MPI-INF-3DHP

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Most implemented papers

CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation

Jianzhuang Liu, Songcen Xu, Zhihao Li, Zhensong Zhang, Youliang Yan•Sun Jul 31 2022

A pseudo-ground-truth annotator based on CLIFF is proposed, which provides high-quality 3D annotations for in-the-wild 2D datasets and offers crucial full supervision for regression-based methods.

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Unsupervised Cross-Modal Alignment for Multi-Person 3D Pose Estimation

R. Venkatesh Babu, Ambareesh Revanur, Jogendra Nath Kundu, Govind V Waghmare, R. Venkatesh•Mon Aug 03 2020

A novel neural representation of multi-person 3D pose which unifies the position of person instances with their corresponding3D pose representation is adopted which yields a superior trade-off between speed and performance, compared to prior top-down approaches.

18 0

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Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation

Maosen Li, Siheng Chen, Ya Zhang, Chenxin Xu•Wed Dec 16 2020

A novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information is proposed and reduces the 3D joint prediction error compared to state-of-the-art unsupervised methods and also outperforms many weakly- supervised methods that use side information on Human3.6M.

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ElePose: Unsupervised 3D Human Pose Estimation by Predicting Camera Elevation and Learning Normalizing Flows on 2D Poses

J. Little, Helge Rhodin, Bastian Wandt•Mon Dec 13 2021

An unsupervised approach that learns to predict a 3D human pose from a single image while only being trained with 2D pose data, which can be crowd-sourced and is already widely available is proposed.

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Occluded Human Body Capture with Self-Supervised Spatial-Temporal Motion Prior

Buzhen Huang, Jingyi Ju, Y. Shu, Yangang Wang•Mon Jul 11 2022

Experimental results show that the key-idea is to employ non-occluded human data to learn a joint-level spatial-temporal motion prior that reduces the ambiguities of occlusions and is robust to diverse occlusion types, which is then adopted to assist the occluded human motion capture.

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Mask as Supervision: Leveraging Unified Mask Information for Unsupervised 3D Pose Estimation

Yuchen Yang, Yu Qiao, Xiao Sun•Mon Dec 11 2023

This work proposes a unified framework that leverages mask as supervision for unsupervised 3D pose estimation that employs skeleton and physique representations that exploit accurate pose information from coarse to fine.

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