Proposed by Yasunori Kudo, Keisuke Ogaki, Yusuke Matsui, Yuri Odagiri at CVPR 2018.Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations (Newly added feature.Combining all the key points JSON files to a continuous sequence with strong baselines.An effective baseline for 3d human pose estimation. Proposed by Julieta Martinez, Rayat Hossain, Javier Romero, James J.Strong Baseline for 3D Human Pose Estimation:.Recoded real-person video input and JSON files collections of motion key points as the output.Proposed by Gines Hidalgo, Zhe Cao, Tomas Simon, Shih-En Wei, Hanbyul Joo, and Yaser Sheikh at CVPR 2017.3D Single-person Key Points Detection (OpenPose):.Some implementations are the edited version of the original for better performance in the application. The output of the previous model will be fed as the input of the following. The project implements multiple Deep Learning Models as a sequential chain. They are stated in the Features section below. There are three intermediate pre-trained Deep Learning Models in the box to process and convert formatted data. OpenPose and MMD are only the 'entrance' and 'exit' of the application box.MMD is a freeware animation program that lets users animate and create 3D animated movies using 3D models like Miku and Anmicius.OpenPose is the first real-time multi-person system proposed by Carnegie Mellon University used to jointly detect human body key points on single images or videos.In short, you record a piece of video with human motions, through this project you will see a 3D model acting the same motions as what you do in the video. OpenMMD can be referred as OpenPose + MikuMikuDance (MMD). OpenMMD represents the OpenPose-Based Deep-Learning project that can directly convert real-person videos to the motion of animation models (i.e.

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