3D Human Pose Estimation
305 papers with code • 25 benchmarks • 46 datasets
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.
Benchmarks
These leaderboards are used to track progress in 3D Human Pose Estimation
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Libraries
Use these libraries to find 3D Human Pose Estimation models and implementationsDatasets
Human3.6M
Waymo Open Dataset
3DPW
AMASS
MPI-INF-3DHP
DensePose
LSP
Panoptic
AGORA
TotalCapture
Subtasks
Latest papers
EventEgo3D: 3D Human Motion Capture from Egocentric Event Streams
In response to the existing limitations, this paper 1) introduces a new problem, i. e., 3D human motion capture from an egocentric monocular event camera with a fisheye lens, and 2) proposes the first approach to it called EventEgo3D (EE3D).
KTPFormer: Kinematics and Trajectory Prior Knowledge-Enhanced Transformer for 3D Human Pose Estimation
This paper presents a novel Kinematics and Trajectory Prior Knowledge-Enhanced Transformer (KTPFormer), which overcomes the weakness in existing transformer-based methods for 3D human pose estimation that the derivation of Q, K, V vectors in their self-attention mechanisms are all based on simple linear mapping.
A Survey on 3D Egocentric Human Pose Estimation
facebookresearch/xR-EgoPose
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Egocentric human pose estimation aims to estimate human body poses and develop body representations from a first-person camera perspective.
GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction
High Dynamic Range (HDR) content (i. e., images and videos) has a broad range of applications.
A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation
Furthermore, the pose estimator's optimization is not exposed to domain shifts, limiting its overall generalization ability.
Disentangled Diffusion-Based 3D Human Pose Estimation with Hierarchical Spatial and Temporal Denoiser
To address these problems, a Disentangled Diffusion-based 3D Human Pose Estimation method with Hierarchical Spatial and Temporal Denoiser is proposed, termed DDHPose.
Deep Learning for 3D Human Pose Estimation and Mesh Recovery: A Survey
To the best of our knowledge, this survey is arguably the first to comprehensively cover deep learning methods for 3D human pose estimation, including both single-person and multi-person approaches, as well as human mesh recovery, encompassing methods based on explicit models and implicit representations.
Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot
naver/multi-hmr
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We present Multi-HMR, a strong single-shot model for multi-person 3D human mesh recovery from a single RGB image.
Lester: rotoscope animation through video object segmentation and tracking
This article introduces Lester, a novel method to automatically synthetise retro-style 2D animations from videos.
Towards Precise 3D Human Pose Estimation with Multi-Perspective Spatial-Temporal Relational Transformers
Due to the challenges in data collection, mainstream datasets of 3D human pose estimation are primarily composed of multi-view video data collected in laboratory environments, which contains rich spatial-temporal correlation information besides the image frame content.
