https://github.com/ZhangNYG/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Papers with Code 아카이브
3D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/Zhangyanbo/iResNetLab
https://github.com/ZhaoHengrun/EDVR
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/ZiqiaoPeng/EmoTalk
EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
https://github.com/a2824256/singleshotpose_imp
https://github.com/aba450/Super-Resolution
https://github.com/abdulwaheedsoudagar/SR-CNN
https://github.com/abhi1kumar/hrnet_pose_single_gpu
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/adbobes/VideoSuperResolution
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
https://github.com/agnJason/RGB-D-PIFuHD
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization
https://github.com/agnesdeng/mixgb
https://github.com/ahmedosman/STAR
VIBE: Video Inference for Human Body Pose and Shape Estimation
https://github.com/ai-forever/kandinskyvideo
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/ajda1992/convformer
https://github.com/akanazawa/hmr
https://github.com/akashpalrecha/deblurring-pipeline
https://github.com/akashpalrecha/superres-deformable
https://github.com/alexjc/neural-enhance
https://github.com/alililia/ascend_ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/alililia/ascend_RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/allenai/satlas-super-resolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/alterzero/DBPN-Pytorch
https://github.com/alterzero/RBPN-PyTorch
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/alterzero/STARnet
https://github.com/amanshenoy/image-super-resolution
https://github.com/amogh7joshi/media-vision
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/amzamzamzamz/nagadomi-waifu2x
https://github.com/anchen1011/toflow
https://github.com/andreacoppari/ELSR-torch
ELSR: Extreme Low-Power Super Resolution Network For Mobile Devices
https://github.com/andrewjong/SwapNet
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
https://github.com/anfidthtn/HAC
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
https://github.com/anibali/h36m-fetch
https://github.com/anilarmagan/HANDS19-Challenge-Toolbox
https://github.com/anktplwl91/Image-Superresolution
https://github.com/anshky/HR-NET
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/antheali/3dpartassembly
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.