#Real-time

OpenPose detects 135 keypoints for body, hands, face, and feet in real-time across multiple subjects in both 2D and 3D. The system supports diverse input sources such as images, videos, and webcams, and offers consistent runtime performance. It is compatible with Ubuntu, Windows, and macOS and utilizes CUDA and OpenCL for improved performance. OpenPose can be integrated via C++ and Python APIs, offering flexibility for input and output processes, suitable for computer vision and AI research.

FasterLivePortrait delivers real-time portrait animation on RTX 3090 using TensorRT, achieving over 30 FPS. It supports cross-platform deployment with ONNX models, providing approximately 12 FPS. The project enhances functionality by supporting native Gradio apps, multi-face handling, and animal models. Recent updates focus on speed optimization and bug fixes. Deployment is simplified with Docker, a Windows package, and macOS support for M1/M2 chips. Ideal for diverse AI applications.

The project introduces LitePose, an advanced architecture for multi-person pose estimation tailored for edge devices. By refining HRNet-based models, it adopts a streamlined single-branch design enhancing both efficiency and performance. Methods such as Fusion Deconv Head and Large Kernel Convs are utilized to minimize redundancy and boost model capacity. LitePose achieves higher mAP with a 25% computation increase compared to earlier models and significantly lowers latency on mobile platforms, promoting real-time estimation without compromising performance.

The GPS-Gaussian project presents a flexible 3D Gaussian method for real-time generation of novel views of unseen characters without extra adjustments. Perfect for scenarios demanding quick multi-angle human renderings, it involves straightforward installation and can be optimized with CUDA to enhance speed. It supports strong training with extensive datasets and includes pretrained models for fast testing. This facilitates rapid deployment on synthetic as well as actual world data, ensuring seamless novel view rendering.

This open-source project facilitates real-time voice conversion using advanced AI technologies on platforms including Windows, Mac, Linux, and Google Colab. Features such as Beatrice v2 and crossfade adjustment enhance functionality, while network load offloading ensures efficiency in demanding applications. Users have the flexibility to use pre-built binaries or set up environments with Docker or Anaconda, optimizing performance for AI models like MMVC and RVC.