https://github.com/HeCraneChen/3D-Crowd-Pose-Estimation-Based-on-MVG
공식 코드Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry
Papers with Code 아카이브
3D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 9
- 평가표
- 25
- 보존된 평가 기록
- 447
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/Healthcare-Robotics/bodies-at-rest
공식 코드Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
https://github.com/HendrikMuenster/superResolution
공식 코드https://github.com/HongwenZhang/DaNet-DensePose2SMPL
공식 코드https://github.com/HongwenZhang/PyMAF
공식 코드PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
https://github.com/IDEA-Research/HumanArt
공식 코드Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes
https://github.com/IDEA-Research/OSX
공식 코드One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer
https://github.com/IIT-PAVIS/lifting_events_to_3d_hpe
공식 코드https://github.com/IRVLab/unrolling
공식 코드IMU-Assisted Learning of Single-View Rolling Shutter Correction
https://github.com/JaeYeonKang/STVUN-Pytorch
공식 코드https://github.com/Jeff-sjtu/HybrIK
공식 코드https://github.com/Jeff-sjtu/res-loglikelihood-regression
공식 코드Human Pose Regression with Residual Log-likelihood Estimation
https://github.com/Jeff-sjtu/sampling-argmax
공식 코드https://github.com/JihuaPeng/KTPFormer
공식 코드https://github.com/JihyongOh/FISR
공식 코드FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal Loss
https://github.com/JimmySuen/integral-human-pose
공식 코드https://github.com/JinluZhang1126/MixSTE
공식 코드MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video
https://github.com/JunukCha/MultiPerson
공식 코드Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement
https://github.com/KHB1698/DC-GCT
공식 코드Double-chain Constraints for 3D Human Pose Estimation in Images and Videos
https://github.com/Khrylx/EgoPose
공식 코드https://github.com/KlabCMU/kin-poly
공식 코드Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation
https://github.com/Lakonik/MVEdit
공식 코드Generic 3D Diffusion Adapter Using Controlled Multi-View Editing
https://github.com/LongguangWang/SOF-VSR
공식 코드Deep Video Super-Resolution using HR Optical Flow Estimation
https://github.com/LongguangWang/SOF-VSR-Super-Resolving-Optical-Flow-for-Video-Super-Resolution-
공식 코드Learning for Video Super-Resolution through HR Optical Flow Estimation
https://github.com/MehwishG/T3D-CNN
공식 코드Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework
https://github.com/MichiganCOG/Simulation_Physical_Plausibility
공식 코드Measuring Physical Plausibility of 3D Human Poses Using Physics Simulation
https://github.com/MichiganCOG/Surgical_Hands_RELEASE
공식 코드Temporally Guided Articulated Hand Pose Tracking in Surgical Videos
https://github.com/Microsoft/human-pose-estimation.pytorch
공식 코드Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/MingjieWe/PGFormer
공식 코드Learning Pyramid-structured Long-range Dependencies for 3D Human Pose Estimation
https://github.com/MohameHassan/PROX
공식 코드Resolving 3D Human Pose Ambiguities with 3D Scene Constraints
https://github.com/MoyGcc/hpcwild
공식 코드https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020
공식 코드Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
https://github.com/NYXFLOWER/TIP
공식 코드Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
https://github.com/Nicholasli1995/EvoSkeleton
공식 코드Cascaded deep monocular 3D human pose estimation with evolutionary training data
https://github.com/NikolaZubic/2dimageto3dmodel
공식 코드An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering
https://github.com/Nithin-GK/UniteandConquer
공식 코드Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.