Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild
Vid2Avatar is a cutting-edge project designed to reconstruct 3D avatars from videos captured in everyday settings. This innovative approach is detailed in a 2023 paper presented at the CVPR conference, and is the result of collaborative efforts by researchers including Chen Guo, Tianjian Jiang, Xu Chen, Jie Song, and Otmar Hilliges. The project adopts self-supervised scene decomposition methods, emphasizing flexibility and adaptability across different environments.
Getting Started
For individuals interested in exploring Vid2Avatar, the process begins with cloning the project's official repository from GitHub. Users need to set up a Python virtual environment, ensuring compatibility with Python 3.7, and install necessary dependencies. One critical component is Kaolin, a library for 3D deep learning that aids in the setup process. The project also requires the SMPL model, a parametric model for human body shape and pose, which can be downloaded separately and configured for use.
Demo Data Utilization
To ease the experiment process, Vid2Avatar offers preprocessed demo data that can be readily used. This includes a pre-trained checkpoint that simplifies the initial exploration, allowing users to focus on evaluating the model's performance without the need for immediate personal data.
Training and Testing
Training the model requires adjusting configurations in specific YAML files to match the dataset, which typically involves video data. Once set up, training can take from 24 to 48 hours, with outputs available for validation and analysis in designated folders. After training, testing can be performed to fine-tune and obtain the final 3D reconstructions.
3D Visualization
For visualizing the results, Vid2Avatar employs AITViewer, a specialized tool for rendering 3D models. Users can switch between static and dynamic modes to observe different aspects of their reconstructed avatars. This visualization aids in understanding both the structural integrity and animation capabilities of the avatars.
Custom Video Processing
Vid2Avatar also supports processing custom videos, making it a versatile tool for personal and professional projects. This involves using tools like ROMP and OpenPose to extract initial shape and pose data. Custom video frames are placed in specific directories, and preprocessing scripts are adapted to fit the unique aspects of the new video content. After preprocessing, users proceed with training and testing in a manner consistent with the standard workflow.
Acknowledgements and Related Works
The project builds upon the foundations of several other research efforts and acknowledges their contributions. These include works such as VolSDF, NeRF++, SMPL-X, and others, which have paved the way for developments in 3D avatar reconstruction.
Vid2Avatar is related to other notable projects like InstantAvatar, X-Avatar, and Hi4D, which also focus on human avatar creation and interactive 3D representations. These projects illustrate a growing field in computer vision and pattern recognition, offering robust solutions for real-world applications.
Through meticulous development and community collaboration, Vid2Avatar stands out as a pivotal tool for transforming ordinary video footage into dynamic 3D avatars, expanding opportunities in entertainment, virtual reality, and beyond.