Papers with Code 아카이브
3D Human Pose Estimation
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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/msu-video-group/erqa
공식 코드ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution
https://github.com/msu-video-group/srdm
공식 코드Combining Contrastive and Supervised Learning for Video Super-Resolution Detection
https://github.com/muelea/shapy
공식 코드Accurate 3D Body Shape Regression using Metric and Semantic Attributes
https://github.com/multimodallearning/da-3dhpe-anatomy
공식 코드Anatomy-guided domain adaptation for 3D in-bed human pose estimation
https://github.com/nankingjing/PoseMamba
공식 코드https://github.com/nationalgailab/hot
공식 코드Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation
https://github.com/naver/multi-hmr
공식 코드Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot
https://github.com/naver/posebert
공식 코드PoseBERT: A Generic Transformer Module for Temporal 3D Human Modeling
https://github.com/nichenxingmeng/STDAN
공식 코드Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution
https://github.com/nicolasugrinovic/size_depth_disambiguation
공식 코드Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images
https://github.com/nies14/rs-net
공식 코드Regular Splitting Graph Network for 3D Human Pose Estimation
https://github.com/nies14/spatio-temporal-mlp-graph
공식 코드Spatio-temporal MLP-graph network for 3D human pose estimation
https://github.com/nikosvasilik/tape
공식 코드TAPE: Temporal Attention-based Probabilistic human pose and shape Estimation
https://github.com/nkolot/ProHMR
공식 코드https://github.com/noboevbo/pedrec
공식 코드PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimation
https://github.com/nqian1/survey-on-odisr-and-odvsr
공식 코드A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution
https://github.com/nvasilik/d-pose
공식 코드D-PoSE: Depth as an Intermediate Representation for 3D Human Pose and Shape Estimation
https://github.com/nvidia-cosmos/cosmos-reason1
공식 코드Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning
https://github.com/nvlabs/glamr
공식 코드GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras
https://github.com/open-debin/mm2latent
공식 코드MM2Latent: Text-to-facial image generation and editing in GANs with multimodal assistance
https://github.com/open-mmlab/mmediting
공식 코드BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
https://github.com/open-mmlab/mmpose
공식 코드RTMW: Real-Time Multi-Person 2D and 3D Whole-body Pose Estimation
https://github.com/opengvlab/hulk
공식 코드Hulk: A Universal Knowledge Translator for Human-Centric Tasks
https://github.com/opengvlab/humanbench
공식 코드Hulk: A Universal Knowledge Translator for Human-Centric Tasks
https://github.com/ostadabbas/adaptedhumanpose
공식 코드Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose Data
https://github.com/ostadabbas/hw-hup
공식 코드https://github.com/ostadabbas/infant-postural-symmetry
공식 코드Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotations
https://github.com/ostadabbas/tepose
공식 코드Live Stream Temporally Embedded 3D Human Body Pose and Shape Estimation
https://github.com/paTRICK-swk/Pose3D-RIE
공식 코드Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose Estimation
https://github.com/patrick-swk/d3dp
공식 코드Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation
https://github.com/patrick-swk/p-stmo
공식 코드P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation
https://github.com/paulchhuang/bstro
공식 코드https://github.com/paulchhuang/rich_toolkit
공식 코드https://github.com/picsart-ai-research/videoinr-continuous-space-time-super-resolution
공식 코드VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution
https://github.com/pixelite1201/CameraHMR/tree/master/CameraHMR_demo
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.