Papers with Code 아카이브
3D Human Pose Estimation
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- 25
- 보존된 평가 기록
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3D Human Pose Estimation is a computer vision task that involves estimating the 3D positions and orientations of body joints and bones from 2D images or videos. The goal is to reconstruct the 3D pose of a person in real-time, which can be used in a variety of applications, such as virtual reality, human-computer interaction, and motion analysis.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xingyizhou/pose-hg-3d
공식 코드Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach
https://github.com/xingyu-song/q_gcn
공식 코드Quater-GCN: Enhancing 3D Human Pose Estimation with Orientation and Semi-supervised Training
https://github.com/xinntao/EDVR
공식 코드EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/xinntao/ESRGAN
공식 코드ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/xinntao/Real-ESRGAN
공식 코드Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/xiu-cs/iganet
공식 코드Interweaved Graph and Attention Network for 3D Human Pose Estimation
https://github.com/xpixelgroup/rethinkvsralignment
공식 코드https://github.com/xtudbxk/tmp
공식 코드TMP: Temporal Motion Propagation for Online Video Super-Resolution
https://github.com/xunshanman/mvgformer
공식 코드Multiple View Geometry Transformers for 3D Human Pose Estimation
https://github.com/xuxy09/RSC-Net
공식 코드3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning
https://github.com/xuxy09/smpler
공식 코드SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation
https://github.com/xy-boy/blind-satellite-vsr
공식 코드https://github.com/xy-boy/lgtd
공식 코드Local-Global Temporal Difference Learning for Satellite Video Super-Resolution
https://github.com/xy-boy/tdmvsr
공식 코드Local-Global Temporal Difference Learning for Satellite Video Super-Resolution
https://github.com/xzhouzeng/prpose
공식 코드Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose Estimation
https://github.com/yanghb22-fdu/hi3d-official
공식 코드Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models
https://github.com/yangsenius/int_hmr_model
공식 코드Capturing the motion of every joint: 3D human pose and shape estimation with independent tokens
https://github.com/yangzhou12/ncrl
공식 코드None Class Ranking Loss for Document-Level Relation Extraction
https://github.com/yc4ny/SVAD
공식 코드https://github.com/yhw-yhw/talkshow
공식 코드https://github.com/yihui-he/epipolar-transformers
공식 코드https://github.com/yiranran/Audio-driven-TalkingFace-HeadPose
공식 코드Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose
https://github.com/yisuanwang/idea23d
공식 코드Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs
https://github.com/yu-jingrui/NToP
공식 코드https://github.com/yuehan717/realviformer
공식 코드RealViformer: Investigating Attention for Real-World Video Super-Resolution
https://github.com/yuezijie/stinet-space-time-video-super-resolution
공식 코드Enhancing Space-time Video Super-resolution via Spatial-temporal Feature Interaction
https://github.com/yufu-wang/tram
공식 코드TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos
https://github.com/yuliangxiu/icon
공식 코드https://github.com/yumingj/C2-Matching
공식 코드Reference-based Image and Video Super-Resolution via C2-Matching
https://github.com/yunfanLu/INR-Event-VSR
공식 코드Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution
https://github.com/yunfanlu/hr-inr
공식 코드HR-INR: Continuous Space-Time Video Super-Resolution via Event Camera
https://github.com/yuqiao0303/fancy123
공식 코드Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation
https://github.com/yuxwind/bigraphbody
공식 코드Joint 3D Human Shape Recovery and Pose Estimation from a Single Image with Bilayer Graph
https://github.com/yw0208/STAF
공식 코드STAF: 3D Human Mesh Recovery from Video with Spatio-Temporal Alignment Fusion
https://github.com/yw0208/W-HMR
공식 코드W-HMR: Monocular Human Mesh Recovery in World Space with Weak-Supervised Calibration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.