https://github.com/yxymessi/dtc123
공식 코드Diffusion Time-step Curriculum for One Image to 3D Generation
Papers with Code 아카이브
3D Human Pose Estimation
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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/yysijie/st-gcn
공식 코드Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/zaedulislam/gs-net
공식 코드Iterative Graph Filtering Network for 3D Human Pose Estimation
https://github.com/zahrag/SLOPE-KP
공식 코드Self-supervised learning of object pose estimation using keypoint prediction
https://github.com/zczcwh/DL-HPE
공식 코드https://github.com/zczcwh/PoseFormer
공식 코드3D Human Pose Estimation with Spatial and Temporal Transformers
https://github.com/zczcwh/feater
공식 코드FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER
https://github.com/zczcwh/gtrs
공식 코드A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose
https://github.com/zengbohan0217/ipdreamer
공식 코드IPDreamer: Appearance-Controllable 3D Object Generation with Complex Image Prompts
https://github.com/zengwang430521/DecoMR
공식 코드https://github.com/zengwang430521/tcformer
공식 코드Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer
https://github.com/zetyquickly/DensePoseFnL
공식 코드https://github.com/zhangchenxu528/FACIAL
공식 코드FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learning
https://github.com/zhangyux15/4d_association
공식 코드4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras
https://github.com/zhaorw02/FlexiDreamer
공식 코드https://github.com/zhengchen1999/dove
공식 코드DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
https://github.com/zhezh/adafuse-3d-human-pose
공식 코드AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild
https://github.com/zhitengli/bidrn
공식 코드BinaryHPE: 3D Human Pose and Shape Estimation via Binarization
https://github.com/zhouxian/act3d-chained-diffuser
공식 코드Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
https://github.com/zhuyue0324/decanus-to-legatus
공식 코드Decanus to Legatus: Synthetic training for 2D-3D human pose lifting
https://github.com/ziangcao0312/PhysX
공식 코드https://github.com/ziniuwan/maed
공식 코드Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation
https://github.com/zj-binxia/ssl
공식 코드Structured Sparsity Learning for Efficient Video Super-Resolution
https://github.com/zmzhang1998/Real-RawVSR
공식 코드Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset
https://github.com/zshn25/pc4consistentdepth
공식 코드Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion
https://gitlab.com/Percipiote/VoxelKeypointFusion
공식 코드VoxelKeypointFusion: Generalizable Multi-View Multi-Person Pose Estimation
https://huggingface.co/datasets/sankalpsinha77/MARVEL-40M
공식 코드MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
https://github.com/07Agarg/Image-Resolution-Enhancement-SRCNN
https://github.com/0jason000/CLIFF
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
https://github.com/1082-datascience/finalproject-finalproject-1082ds_group3
https://github.com/15517965908/keras-DBPN
https://github.com/1zgh/st-gcn
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/20151001860/Real_ESRGAN_paddle
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DBPN
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/ESRGAN
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.