https://github.com/chaitanya100100/TailorNet
공식 코드TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style
Papers with Code 아카이브
3D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/chaneyddtt/Generating-Multiple-Hypotheses-for-3D-Human-Pose-Estimation-with-Mixture-Density-Network
공식 코드Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Network
https://github.com/chaneyddtt/weakly-supervised-3d-pose-generator
공식 코드Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses
https://github.com/changlongjianggit/a2j-transformer
공식 코드https://github.com/charlesnchr/vsr-sim
공식 코드Spatio-temporal Vision Transformer for Super-resolution Microscopy
https://github.com/chengtan9907/OpenSTL
공식 코드https://github.com/chenhongyiyang/egoposeformer
공식 코드EgoPoseFormer: A Simple Baseline for Stereo Egocentric 3D Human Pose Estimation
https://github.com/chensong1995/deblursr
공식 코드DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation
https://github.com/chenvoid/diffusiontalker
공식 코드https://github.com/chiyich/egovsr
공식 코드EgoVSR: Towards High-Quality Egocentric Video Super-Resolution
https://github.com/circejie/hdifftg
공식 코드HDiffTG: A Lightweight Hybrid Diffusion-Transformer-GCN Architecture for 3D Human Pose Estimation
https://github.com/ckkelvinchan/basicvsr_plusplus
공식 코드On the Generalization of BasicVSR++ to Video Deblurring and Denoising
https://github.com/ckkelvinchan/realbasicvsr
공식 코드Investigating Tradeoffs in Real-World Video Super-Resolution
https://github.com/claudiom4sir/stablevsr
공식 코드https://github.com/climbingdaily/SLOPER4D
공식 코드SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments
https://github.com/climbingdaily/hsc4d
공식 코드https://github.com/clovaai/meev
공식 코드https://github.com/codeslake/RefVSR
공식 코드Reference-based Video Super-Resolution Using Multi-Camera Video Triplets
https://github.com/continental/6img-to-3d
공식 코드6Img-to-3D: Few-Image Large-Scale Outdoor Driving Scene Reconstruction
https://github.com/coulsonlee/stdo-cvpr2023
공식 코드Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting
https://github.com/csbhr/Deep-Blind-VSR
공식 코드https://github.com/csbhr/self-blind-vsr
공식 코드https://github.com/cseeyangchen/dt-pose
공식 코드Towards Robust and Realistic Human Pose Estimation via WiFi Signals
https://github.com/csxmli2016/DFDNet
공식 코드Blind Face Restoration via Deep Multi-scale Component Dictionaries
https://github.com/cszn/BSRGAN
공식 코드Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
https://github.com/cszn/SRMD
공식 코드Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
https://github.com/cure-lab/DeciWatch
공식 코드DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation
https://github.com/cure-lab/SmoothNet
공식 코드SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos
https://github.com/cvlab-columbia/zero123
공식 코드https://github.com/cvlab-epfl/segmentation-driven-pose
공식 코드https://github.com/cyk990422/bopr
공식 코드BoPR: Body-aware Part Regressor for Human Shape and Pose Estimation
https://github.com/dachunkai/ev-deblurvsr
공식 코드https://github.com/dachunkai/evtexture
공식 코드EvTexture: Event-driven Texture Enhancement for Video Super-Resolution
https://github.com/danielajisafe/mirror-aware-neural-humans
공식 코드https://github.com/davidpengucf/daf-dg
공식 코드A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation
https://github.com/deepgogogo/CAGNet
공식 코드Consistency-Aware Graph Network for Human Interaction Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.