This work is very much like hmr/vibe in the following ways:

• Overall network architecture: Encoder + Regressor + Discriminator
• Loss function: 2D reprojection loss + SMPL parameter loss + 3D keypoints loss + discriminator loss
• Dataset: 2D annotated data + 3D annotated data + unpaired data (for GAN)
• Source code: data processing by hmr

The different part of this work:

• A different network in detail: different encoder and regressor
• A simpler way to train: start training on any Linux/Unix environment by running one line of bash command! You don't even need to download this repository.

Simply run bash command wget https://github.com/wolverinn/human-pose-estimation-GAN/raw/master/auto.sh && bash auto.sh and the training will be on !

The command will do dataset downloading, directory creating, source code downloading and running.

Small checks before training:

• Python3
• Tensorflow==1.14
• Check the device for "cpu" or "gpu" in src/main.py after you run the command above.
• Check start.bash to start training !

• 人体姿态估计的过去，现在，未来 - 知乎
• 3D人体姿态估计笔记 - 知乎
• 基于深度学习的3D pose estimation总结
• 人体模型介绍 - SMPL
• Paper with code
• awesome human pose estimation - GitHub
• 弱透视投影 - CSDN
• 配置opendr问题