https://github.com/aimi-lab/robust-pose-estimator
공식 코드Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
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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.
Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
3D Semantic Scene Perception using Distributed Smart Edge Sensors
3D Semantic Scene Perception using Distributed Smart Edge Sensors
Synthetic Training for Accurate 3D Human Pose and Shape Estimation in the Wild
PoseGravity: Pose Estimation from Points and Lines with Axis Prior
Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species
OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detection
Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
Deep Reinforcement Learning for Active Human Pose Estimation
Setup-Invariant Augmented Reality for Teaching by Demonstration with Surgical Robots
AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling
EventGAN: Leveraging Large Scale Image Datasets for Event Cameras
Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding
FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution
ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose Estimation
Mixture Dense Regression for Object Detection and Human Pose Estimation
Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation
Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity
HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields
End-to-End Trainable Multi-Instance Pose Estimation with Transformers
RelPose++: Recovering 6D Poses from Sparse-view Observations
MRC-Net: 6-DoF Pose Estimation with MultiScale Residual Correlation
Shape Preserving Facial Landmarks with Graph Attention Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.