Monocular Total Capture: A Comprehensive Introduction
Monocular Total Capture is a fascinating project presented in a CVPR 2019 paper, aimed at capturing detailed poses of the face, body, and hands from a monocular video source. This project is pioneering in its ability to perform such comprehensive capture in dynamic and uncontrolled environments, often referred to as "in the wild."
Project Overview
The Monocular Total Capture project showcases advanced computer vision techniques that allow the successful reconstruction of full-body motion, facial expressions, and hand gestures using a single camera. This revolutionary approach leverages a blend of computational models and human body frameworks to interpret pose data accurately.
Dependencies and Requirements
To run the Monocular Total Capture code, a setup on a Linux-based system (specifically tested on Ubuntu 16.04) is recommended. A powerful graphics card (NVIDIA GTX 1080Ti or equivalent) is necessary for processing, along with several software prerequisites:
- Programming Languages and Tools: Python 3.5 with TensorFlow 1.5.0, OpenCV, cmake, OpenGL, and related libraries.
- Additional Software: Ceres-Solver, libigl, OpenPose for pose detection, and utilities like ffmpeg and wget for video handling.
Installation Steps
Installing Monocular Total Capture involves obtaining the code repository, handling dependencies, and setting up various configurations. A summary of the process includes:
- Cloning the repository and preparing the environment.
- Downloading necessary data and installing OpenPose, a vital component for pose estimation in the pipeline.
- Editing specified configuration files to direct the system to necessary resources, such as libigl's include paths.
- Building the software using cmake and make commands to compile the necessary binaries for operation.
Using the Software
To test with a video, users need to prepare the video file and camera parameters (if known) in a specified directory format. Utilizing the installation, users run pipeline scripts provided, which process the video data for pose analysis. Special options, like focusing on upper body analysis, are available for tailored tasks.
Docker Support
The Monocular Total Capture project can be run via a Docker container, ensuring a portable and consistent environment. Users build a Docker image and run it, allowing direct use of the project's capabilities within the containerized setup.
Example Datasets
The project provides example videos for new users to test the installation and become familiar with the system's capabilities. Sample commands demonstrate typical usage scenarios, such as interpreting dance or speech motions.
Licensing and Citation
The project is designated for non-commercial research purposes, and users are encouraged to cite the foundational papers by the creators when using this resource in academic work.
Adam Model
The Monocular Total Capture project utilizes the Adam model, a sophisticated digital representation of human figures. Adam incorporates elements from renowned models like SMPL for body structure and FaceWarehouse for facial topology, albeit with significant adaptations. The model is available for research but has restrictions on commercial use and redistribution.
Special Technical Note
There is a specific consideration regarding the output matrix format when interfacing with other software. Users are advised to invert the pose parameters output by the system to ensure compatibility with external applications.
In summary, Monocular Total Capture offers a unique solution for motion capture using monocular video data, advancing the field of computer vision and enabling new applications in environments that were previously challenging to analyze.