3D Human Pose Estimation

3D Human Pose Estimation is a computer vision task that involves estimating the 3D positions and orientations of body joints and bones from 2D images or videos. The goal is to reconstruct the 3D pose of a person in real-time, which can be used in a variety of applications, such as virtual reality, human-computer interaction, and motion analysis.

Papers

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Showing 251–260 of 665 papers

Title	Date	Tasks	Status
Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape Estimation from Monocular Video	Mar 16, 2022	3D human pose and shape estimation3D Human Pose Estimation	—Unverified
3D Human Pose Estimation Using Möbius Graph Convolutional Networks	Mar 20, 2022	3D Human Pose EstimationPose Estimation	—Unverified
Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos	Apr 9, 2016	3D Human Pose EstimationEmotion Recognition	—Unverified
FusePose: IMU-Vision Sensor Fusion in Kinematic Space for Parametric Human Pose Estimation	Aug 25, 2022	3D Human Pose EstimationPose Estimation	—Unverified
3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information	Aug 10, 2016	2D Human Pose Estimation2D Pose Estimation	—Unverified
Can 3D Pose be Learned from 2D Projections Alone?	Aug 22, 2018	3D Human Pose Estimation3D Pose Estimation	—Unverified
CameraPose: Weakly-Supervised Monocular 3D Human Pose Estimation by Leveraging In-the-wild 2D Annotations	Jan 8, 2023	3D Human Pose EstimationData Augmentation	—Unverified
FBI-Pose: Towards Bridging the Gap between 2D Images and 3D Human Poses using Forward-or-Backward Information	Jun 25, 2018	3D Human Pose Estimation	—Unverified
Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation	Jun 1, 2014	3D Human Pose EstimationPose Estimation	—Unverified
It's all Relative: Monocular 3D Human Pose Estimation from Weakly Supervised Data	May 17, 2018	2D Pose Estimation3D Human Pose Estimation	—Unverified

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All datasets 3DPW MPI-INF-3DHP Human3.6M HumanEva-I H3WB Total Capture EMDB AGORA Panoptic Surreal 3D Poses in the Wild Challenge AIST++

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Simple-baseline	PA-MPJPE	157	—	Unverified
2	HMR	MPJPE	130	—	Unverified
3	BMP	MPVPE	119.3	—	Unverified
4	SPIN	MPVPE	116.4	—	Unverified
5	Wenshuo et a;.	MPVPE	112.6	—	Unverified
6	TCMR (T=16 w/o 3DPW)	MPVPE	111.5	—	Unverified
7	CHOMP	MPVPE	110.1	—	Unverified
8	PC-HMR	MPVPE	108.6	—	Unverified
9	3DCrowdNet	MPVPE	108.5	—	Unverified
10	SMPLify	PA-MPJPE	106.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	VNect (Augm.)	MPJPE	124.7	—	Unverified
2	HMR	MPJPE	124.2	—	Unverified
3	Single-Shot Multi-Person	MPJPE	122.2	—	Unverified
4	Mehta	MPJPE	117.6	—	Unverified
5	PONet	MPJPE	115	—	Unverified
6	Pose Consensus (monocular)	MPJPE	112.1	—	Unverified
7	GeoRep (fully-supervised)	MPJPE	110.8	—	Unverified
8	XFormer (HRNet)	MPJPE	109.8	—	Unverified
9	EpipolarPose (fully-supervised)	MPJPE	108.99	—	Unverified
10	SPIN	MPJPE	105.2	—	Unverified