https://github.com/syguan96/dynaboa
공식 코드Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptation
Papers with Code 아카이브
Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 31
- 보존된 평가 기록
- 235
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/szar-lab/aggpose
공식 코드AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation
https://github.com/szucchini/racewalk-aijudge
공식 코드https://github.com/taatiteam/motionagformer
공식 코드MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network
https://github.com/taewoonglee17/twoviewsdsareg
공식 코드3D/2D Registration of Angiograms using Silhouette-based Differentiable Rendering
https://github.com/take2rohit/poise
공식 코드POISE: Pose Guided Human Silhouette Extraction under Occlusions
https://github.com/take2rohit/stride
공식 코드STRIDE: Single-video based Temporally Continuous Occlusion-Robust 3D Pose Estimation
https://github.com/tang1705/seeclear-neurips24
공식 코드SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution
https://github.com/tangtao-pku/arts
공식 코드https://github.com/tavisshore/PEnG
공식 코드https://github.com/tecsar-uncc/adg-pose
공식 코드ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimation
https://github.com/tencentarc/animesr
공식 코드AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos
https://github.com/tencentarc/fastrealvsr
공식 코드Mitigating Artifacts in Real-World Video Super-Resolution Models
https://github.com/tencentarc/instantmesh
공식 코드https://github.com/tensorlayer/hyperpose
공식 코드https://github.com/tenstep/DeepModel
공식 코드https://github.com/ternaus/robot-surgery-segmentation
공식 코드Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning
https://github.com/texasinstruments/edgeai-yolov5
공식 코드YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss
https://github.com/texasinstruments/edgeai-yolox
공식 코드YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss
https://github.com/tfzhou/MG-HumanParsing
공식 코드https://github.com/theNded/SGP
공식 코드https://github.com/theo-jaunet/sim2realviz
공식 코드SIM2REALVIZ: Visualizing the Sim2Real Gap in Robot Ego-Pose Estimation
https://github.com/thienannguyen-cv/2D-representation-experiments/blob/main/experiment-code.ipynb
공식 코드A Solution for a Fundamental Problem of 3D Inference based on 2D Representations
https://github.com/tho-kn/Ego3DPose
공식 코드Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views
https://github.com/tho-kn/egotap
공식 코드Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting
https://github.com/thodan/bop_toolkit
공식 코드https://github.com/thodan/epos
공식 코드https://github.com/thodan/t-less_toolkit
공식 코드T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects
https://github.com/thohemp/6drepnet
공식 코드6D Rotation Representation For Unconstrained Head Pose Estimation
https://github.com/thohemp/6drepnet360
공식 코드Towards Robust and Unconstrained Full Range of Rotation Head Pose Estimation
https://github.com/thomasgolda/Human-Pose-Estimation-for-Real-World-Crowded-Scenarios
공식 코드https://github.com/thomasverelst/dynconv
공식 코드Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference
https://github.com/thu-da-6d-pose-group/catre
공식 코드CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement
https://github.com/thu-da-6d-pose-group/self6dpp
공식 코드Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
https://github.com/thuml/Regressive-Domain-Adaptation-for-Unsupervised-Keypoint-Detection
공식 코드Regressive Domain Adaptation for Unsupervised Keypoint Detection
https://github.com/thunil/TecoGAN
공식 코드Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.