mirror of https://github.com/open-mmlab/mmpose
81 lines
3.3 KiB
Markdown
81 lines
3.3 KiB
Markdown
# Awesome MMPose
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A list of resources related to MMPose. Feel free to contribute!
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<div align=center>
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<img src="https://user-images.githubusercontent.com/13503330/231416285-5467d313-0732-4ada-97e1-12be6ec69a28.png" width="800"/>
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</div><br/>
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## Contents
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- [Tutorials](#tutorials)
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- [Papers](#papers)
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- [Datasets](#datasets)
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- [Projects](#projects)
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## Tutorials
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- [MMPose Tutorial (Chinese)](https://github.com/TommyZihao/MMPose_Tutorials)
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MMPose 中文视频代码教程，from 同济子豪兄
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<div align=center>
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<img src="https://user-images.githubusercontent.com/13503330/231640277-777f611c-b3d9-4d41-830f-8e48a352fd01.jpg" width="500"/>
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</div><br/>
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- [OpenMMLab Course](https://github.com/open-mmlab/OpenMMLabCourse)
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This repository hosts articles, lectures and tutorials on computer vision and OpenMMLab, helping learners to understand algorithms and master our toolboxes in a systematical way.
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## Papers
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- [\[paper\]](https://arxiv.org/abs/2207.10387) [\[code\]](https://github.com/luminxu/Pose-for-Everything)
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ECCV 2022, Pose for Everything: Towards Category-Agnostic Pose Estimation
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- [\[paper\]](https://arxiv.org/abs/2201.04676) [\[code\]](https://github.com/Sense-X/UniFormer)
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ICLR 2022, UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning
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- [\[paper\]](https://arxiv.org/abs/2201.07412) [\[code\]](https://github.com/aim-uofa/Poseur)
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ECCV 2022, Poseur:Direct Human Pose Regression with Transformers
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- [\[paper\]](https://arxiv.org/abs/2106.03348) [\[code\]](https://github.com/ViTAE-Transformer/ViTAE-Transformer)
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NeurIPS 2022, ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond
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- [\[paper\]](https://arxiv.org/abs/2204.10762) [\[code\]](https://github.com/ZiyiZhang27/Dite-HRNet)
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IJCAI-ECAI 2021, Dite-HRNet:Dynamic Lightweight High-Resolution Network for Human Pose Estimation
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- [\[paper\]](https://arxiv.org/abs/2302.08453) [\[code\]](https://github.com/TencentARC/T2I-Adapter)
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T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
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- [\[paper\]](https://arxiv.org/pdf/2303.11638.pdf) [\[code\]](https://github.com/Gengzigang/PCT)
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CVPR 2023, Human Pose as Compositional Tokens
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## Datasets
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- [\[github\]](https://github.com/luminxu/Pose-for-Everything) **MP-100**
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Multi-category Pose (MP-100) dataset, which is a 2D pose dataset of 100 object categories containing over 20K instances and is well-designed for developing CAPE algorithms.
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<div align=center>
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<img src="https://user-images.githubusercontent.com/13503330/231639551-b32ed2ab-aec0-4410-937e-c81a2ac2cb0d.png" width="500"/>
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</div><br/>
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- [\[github\]](https://github.com/facebookresearch/Ego4d/) **Ego4D**
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EGO4D is the world's largest egocentric (first person) video ML dataset and benchmark suite, with 3,600 hrs (and counting) of densely narrated video and a wide range of annotations across five new benchmark tasks. It covers hundreds of scenarios (household, outdoor, workplace, leisure, etc.) of daily life activity captured in-the-wild by 926 unique camera wearers from 74 worldwide locations and 9 different countries.
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<div align=center>
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<img src="https://user-images.githubusercontent.com/13503330/231640003-d43028cc-6f83-45e7-b76a-8e8f0cddcfcb.png" width="500"/>
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</div><br/>
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## Projects
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Waiting for your contribution!
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