Official implementation of CVPR2020 paper "VIBE: Video Inference for Human Body Pose and Shape Estimation"
翻译 - CVPR2020文件“ VIBE:用于人体姿势和形状估计的视频推理”的正式实施
Official code of "HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation", CVPR 2021
[ICCV 2023] PyTorch Implementation of "MotionBERT: A Unified Perspective on Learning Human Motion Representations"
We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular c...
#Awesome#😎Awesome list of papers about 3D body
ExPose - EXpressive POse and Shape rEgression
#计算机科学#Self-Supervised Learning of 3D Human Pose using Multi-view Geometry (CVPR2019)
A deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video [ToG 2020]
The Pytorch implementation for "Semantic Graph Convolutional Networks for 3D Human Pose Regression" (CVPR 2019).
#计算机科学#Official Torch7 implementation of "V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map", CVPR 2018
[ECCV 2022] Official implementation of the paper "SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos"
A simple baseline for 3d human pose estimation in PyTorch.
#计算机科学#Official project website for the CVPR 2020 paper (Oral Presentation) "Cascaded deep monocular 3D human pose estimation wth evolutionary training data"
Code for paper "A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image". ICCV2019
#计算机科学#Official repository of Human3.6M 3D WholeBody (H3WB) dataset
State-of-the-art methods on monocular 3D pose estimation / 3D mesh recovery
#计算机科学#[ICLR 2024] MogaNet: Efficient Multi-order Gated Aggregation Network
#计算机科学#Official project website for the CVPR 2021 paper "Exploring intermediate representation for monocular vehicle pose estimation"
#计算机科学#[ECCV 2022] Official implementation of the paper "DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation"
3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks