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RAIN
Overview :
RAIN is a real-time animation technology for infinite video streaming, capable of delivering high quality and low-latency animations on consumer-grade devices. It efficiently calculates frame labeling with different noise levels and extended intervals while denoising more frame labels than traditional streaming methods. This allows for generating video frames at a higher speed and shorter latency while maintaining stream coherence. RAIN introduces only a small number of additional 1D attention blocks, which keeps the system's burden light. This technology is expected to combine with computer graphics (CG) in fields such as game rendering, live streaming, and virtual reality, utilizing AI's generative capabilities to render countless new scenes and objects, providing a more interactive participation experience.
Target Users :
The target audience is users who need to create real-time video animations on consumer-grade devices, such as game developers, video content creators, and live stream hosts. These users can leverage RAIN technology to achieve efficient and smooth real-time animation effects without sacrificing quality, thereby enhancing the attractiveness and interactivity of their content.
Use Cases
Real-time generation of high-quality full-body animations using only 500 video clips trained on the UBC-Fashion dataset.
Mapping real facial expressions and head positions to anime faces to achieve cross-domain facial morphing animation.
Real-time generation of character animations in game live streams to enhance viewer experience.
Features
Real-time animation of infinite video streams on a single RTX 4090 GPU with low latency.
Employs LCM Distillation to accelerate the UNet model, using TAESDV as the VAE decoder.
Achieves general operational speeds of 18 fps with approximately 1.5 seconds of latency through TensorRT acceleration.
Supports the generation of infinitely long videos, maintaining long-term attention for enhanced coherence and consistency.
After fine-tuning the Stable Diffusion model, it can generate high-quality video streams in real time with low latency.
Demonstrates superior quality, accuracy, and consistency compared to competitors in benchmark datasets and ultra-long video generation.
How to Use
1. Obtain the RAIN model and related code, available for download from the project’s GitHub link.
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2. Prepare the necessary hardware, such as an RTX 4090 GPU, along with the corresponding software environment.
3. Accelerate the UNet model using LCM Distillation and configure TAESDV as the VAE decoder.
4. Utilize TensorRT for acceleration and optimize model performance.
5. Input the video stream to be animated into the model, which will process it according to the set noise levels and time intervals.
6. Output animation effects in real time; examples can be viewed through the video link provided by the project.
7. Fine-tune the model as needed to adapt to specific animation styles or application scenarios.
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