

Vidtok
Overview :
VidTok is a series of advanced video segmenters open-sourced by Microsoft, excelling in both temporal and spatial segmentation. It features significant innovations in architectural efficiency, quantization techniques, and training strategies, providing efficient video processing capabilities and outperforming previous models across multiple video quality assessment metrics. The development of VidTok aims to advance video processing and compression technologies, which are crucial for the efficient transmission and storage of video content.
Target Users :
VidTok targets researchers and developers in the video processing field, especially professionals in need of efficient video compression and transmission solutions. With its innovations and efficiency in video segmentation technology, VidTok is well-suited for enterprises and research institutions that handle large volumes of video data, assisting them in optimizing video storage and transmission efficiency.
Use Cases
Video content creators can use VidTok to compress and optimize their videos for more efficient sharing online.
Online video platforms can leverage VidTok's technology to enhance the quality and transmission efficiency of video streams.
Research institutions can further explore video analysis and processing based on VidTok, propelling advancements in video technology.
Features
Efficient Architecture: Reduces computational complexity through separation of spatial and temporal sampling while maintaining video quality.
Advanced Quantization: Employs Finite Scalar Quantization (FSQ) technology to address training instability issues in discrete segmentation.
Enhanced Training: Utilizes a two-stage strategy, first pre-training on low-resolution videos before fine-tuning on high-resolution videos to improve efficiency.
Outstanding Performance: Trained on large-scale video datasets, it outperforms previous models in metrics such as PSNR, SSIM, LPIPS, and FVD.
Flexible Applications: Supports both continuous and discrete segmentation, catering to diverse video compression and processing needs.
Open-Source Model: The code is open-sourced, facilitating secondary development and optimization by researchers and developers.
How to Use
1. Visit VidTok's GitHub page and clone the repository to your local machine.
2. Set up the Conda environment using the provided `environment.yaml` file.
3. Download the pre-trained models and place them in the `checkpoints` folder.
4. Modify the configuration file as needed to set data paths and model parameters.
5. Run the `main.py` script to start training or fine-tuning the model.
6. Use the `scripts/inference_evaluate.py` script to assess video reconstruction performance.
7. Utilize the `scripts/inference_reconstruct.py` script to reconstruct input videos.
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