The editor of Downcodes learned that an open source project called Ultralight-Digital-Human is causing heated discussions on GitHub! This project has made a breakthrough in solving the problem of deploying digital human technology on the mobile terminal, enabling ordinary smartphones to run digital human applications smoothly, greatly lowering the application threshold of digital human technology and bringing unprecedented opportunities for its popularization. This will have a profound impact on mobile application development, as well as AR/VR and other fields.
Recently, an open source project called Ultralight-Digital-Human has attracted widespread attention in the developer community. This project successfully solved the problem of deploying digital human technology on the mobile terminal, allowing ordinary smartphones to run digital human applications in real time, bringing new possibilities to the popularization of related technologies.
This ultra-lightweight digital human model uses innovative deep learning technology, and through algorithm optimization and model compression, it has successfully slimmed down the huge digital human system to the point where it can run smoothly on mobile devices. The system supports real-time processing of video and audio inputs, and can quickly synthesize digital human images with prompt response and smooth operation.
In terms of technical implementation, the project integrates two audio feature extraction solutions, Wenet and Hubert, and developers can flexibly choose according to specific application scenarios. At the same time, through the introduction of synchronization network (syncnet) technology, the lip synchronization effect of digital humans is significantly improved. In order to ensure smooth operation on mobile devices, the development team adopted parameter pruning technology during the training and deployment process, which effectively reduced computing resource requirements.
Another highlight of the project is the complete documentation of the training process. Developers only need to prepare 3-5 minutes of high-quality face videos and follow the guidelines to start training their own digital human models. The system's video requirements are also very clear. Wenet mode requires a frame rate of 20fps, while Hubert mode requires 25fps.
In order to ensure the training effect, the project team specifically reminds developers to pay attention to the following key links: preferred pre-training models as the basis; ensuring the quality of training data; regularly monitoring the training process; and adjusting training parameters in a timely manner. These details will directly affect the final digital human effect.
Currently, this open source project has shown great potential in areas such as social applications, mobile games, and virtual reality. Compared with traditional digital human technology, it not only lowers the hardware threshold, but also achieves cross-platform compatibility and can run stably on all types of smartphones.
Project address: https://github.com/anliyuan/Ultralight-Digital-Human
The Ultralight-Digital-Human project is undoubtedly a milestone in the development of digital human technology, and its open source nature also provides more developers with opportunities to learn and innovate. I believe there will be more innovative applications based on this project in the future, let’s wait and see!