Papers with Code 아카이브
Pose Tracking
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Pose Tracking is the task of estimating multi-person human poses in videos and assigning unique instance IDs for each keypoint across frames. Accurate estimation of human keypoint-trajectories is useful for human action recognition, human interaction understanding, motion capture and animation.
Source: LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/leeyegy/simcc
https://github.com/line/Human-Interaction-Generation
https://github.com/lyqcom/alphapose
https://github.com/mindspore-ai/models/blob/master/research/cv/simple_baselines/
https://github.com/mindspore-ai/models/tree/master/official/cv/simple_pose
https://github.com/mindspore-ai/models/tree/master/research/cv/fairmot
https://github.com/mindspore-lab/mindone
https://github.com/mks0601/PoseFix_RELEASE
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/mks0601/TF-SimpleHumanPose
https://github.com/nageshpindi/human-motion-prediction-master
https://github.com/nkdnnlr/Event-Based-Camera-Simultaneous-Mosaicing-and-Tracking
Event-based Camera Pose Tracking using a Generative Event Model
https://github.com/nvlabs/dancing2music
https://github.com/open-mmlab/mmdetection
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/open-mmlab/mmpose
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
https://github.com/osmr/imgclsmob
https://github.com/p-morais/gym-cassie
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
https://github.com/pairlab/6pack
6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints
https://github.com/pairlab/robosuite
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
https://github.com/papagina/auto_conditioned_rnn_motion
Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis
https://github.com/samson6460/tf2_pose_estimation
https://github.com/sdll/hrnet-pose-estimation
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/simochen/flowtrack.pytorch
https://github.com/simonalexanderson/MoGlow
MoGlow: Probabilistic and controllable motion synthesis using normalising flows
https://github.com/simonalexanderson/StyleGestures
MoGlow: Probabilistic and controllable motion synthesis using normalising flows
https://github.com/strivebo/image_segmentation_dl
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/thomasslloyd/FitSpatial
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/thoughtmachines/Human-Pose-Estimation-using-HRNets
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/umariqb/posetrack-cvpr2017
https://github.com/uzh-rpg/rpg_image_reconstruction_from_events
Event-based Camera Pose Tracking using a Generative Event Model
https://github.com/v1viswan/Domain_adaptation_in_HRNet
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/victimsnino/Simple-Baselines-for-Human-Pose-Estimation-and-Tracking-sample-
https://github.com/victimsnino/pose-simple-baselines-demo.pytorch.
https://github.com/vietanhdev/tf-blazepose
https://github.com/viiika/diffusion-conductor
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
https://github.com/visionNoob/hrnet_pytorch
Deep High-Resolution Representation Learning for Human Pose Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.