2D Human Pose Estimation
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What is Human Pose Estimation? Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. The reason for its importance is the abundance of applications that can benefit from such a technology. For example, human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hnuzhy/ssda-yolo
공식 코드SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection
https://github.com/hoenigpeter/randomized_texturing
공식 코드https://github.com/hologerry/Attr2Font
공식 코드https://github.com/holzbock/dfbf
공식 코드https://github.com/hongsukchoi/3dcrowdnet_release
공식 코드Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes
https://github.com/hongyehu/RG-Flow
공식 코드RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
https://github.com/horsepurve/StyleSegor
공식 코드Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data
https://github.com/house-leo/cfdvsr
공식 코드Collaborative Feedback Discriminative Propagation for Video Super-Resolution
https://github.com/house-leo/ctun
공식 코드Cascaded Temporal Updating Network for Efficient Video Super-Resolution
https://github.com/howiema/ppt
공식 코드PPT: token-Pruned Pose Transformer for monocular and multi-view human pose estimation
https://github.com/hsm316/llm_wikipedia
공식 코드https://github.com/hsuvas/hoaxpedia_dataset
공식 코드https://github.com/huage001/adaattn
공식 코드AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
https://github.com/huage001/dynast
공식 코드DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
https://github.com/huage001/painttransformer
공식 코드Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
https://github.com/huage001/transfer-any-style
공식 코드Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate
https://github.com/huankoh/long-doc-summarization
공식 코드An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics
https://github.com/hubertsgithub/style_painting_robustness
공식 코드What Can Style Transfer and Paintings Do For Model Robustness?
https://github.com/husseinmozannar/SOQAL
공식 코드https://github.com/hybrinfox/ppn
공식 코드https://github.com/hyeonseob-nam/Batch-Instance-Normalization
공식 코드Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
https://github.com/hyhsimon/boosted-vsr
공식 코드Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization
https://github.com/hynky1999/czech-news-classification-dataset
공식 코드A Dataset and Strong Baselines for Classification of Czech News Texts
https://github.com/i2-multimedia-lab/diffusest
공식 코드DiffuseST: Unleashing the Capability of the Diffusion Model for Style Transfer
https://github.com/iamalexkorotin/Wasserstein2GenerativeNetworks
공식 코드https://github.com/iandreafc/gdeltnews
공식 코드https://github.com/ianxmason/local-phases
공식 코드https://github.com/ianyeung/mgld-vsr
공식 코드Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution
https://github.com/ibm-aur-nlp/PubLayNet
공식 코드PubLayNet: largest dataset ever for document layout analysis
https://github.com/ibm-aur-nlp/PubTabNet
공식 코드https://github.com/ictmcg/mtm
공식 코드https://github.com/idea-research/dwpose
공식 코드Effective Whole-body Pose Estimation with Two-stages Distillation
https://github.com/idea-research/ed-pose
공식 코드Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
https://github.com/idea-research/x-pose
공식 코드https://github.com/ielab/agvaluate
공식 코드AgAsk: An Agent to Help Answer Farmer's Questions From Scientific Documents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.