https://github.com/shaohua-pan/RobustCap
공식 코드Fusing Monocular Images and Sparse IMU Signals for Real-time Human Motion Capture
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Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.
A common benchmark for this task is MPII Human Pose
<span style="color:grey; opacity: 0.6">( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Fusing Monocular Images and Sparse IMU Signals for Real-time Human Motion Capture
DeepSTEP: Simultaneous Tracking and Estimation of Pose for Animals and Humans
LAMP: Leveraging Language Prompts for Multi-person Pose Estimation
Sliding Window Recurrent Network for Efficient Video Super-Resolution
Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation
VoxelKP: A Voxel-based Network Architecture for Human Keypoint Estimation in LiDAR Data
ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors
FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models
Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image
Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
MTevent: A Multi-Task Event Camera Dataset for 6D Pose Estimation and Moving Object Detection
A lightweight 3D dense facial landmark estimation model from position map data
Humans in 4D: Reconstructing and Tracking Humans with Transformers
CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in Space
Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"
GoodPoint: unsupervised learning of keypoint detection and description
Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions
Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation
2D Image head pose estimation via latent space regression under occlusion settings
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal Anchors
PlaneRecTR++: Unified Query Learning for Joint 3D Planar Reconstruction and Pose Estimation
Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation
Estimating 6D Pose From Localizing Designated Surface Keypoints
Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE
Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms
SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.