VidTok

A Family of Versatile and State-Of-The-Art Video Tokenizers

VidTok is a cutting-edge family of video tokenizers that delivers state-of-the-art performance in both continuous and discrete tokenizations with various compression rates. VidTok incorporates several key advancements over existing approaches:

• ⚡️ Efficient Architecture. Separate spatial and temporal sampling reduces computational complexity without sacrificing quality.
• 🔥 Advanced Quantization. Finite Scalar Quantization (FSQ) addresses training instability and codebook collapse in discrete tokenization.
• 💥 Enhanced Training. A two-stage strategy—pre-training on low-res videos and fine-tuning on high-res—boosts efficiency. Reduced frame rates improve motion dynamics representation.

VidTok, trained on a large-scale video dataset, outperforms previous models across all metrics, including PSNR, SSIM, LPIPS, and FVD.

Resources and technical documentation:

• GitHub
• arXiv

Model Performance

The following table shows model performance evaluated on 30 test videos in MCL_JCL dataset, with a sample fps of 30. The input size is 17x256x256 for causal models and 16x256x256 for non-causal models. VCR indicates the video compression ratio TxHxW.

Training

Training Data

The training data of VidTok is divided into two sets based on video quality.

1. Training Set 1 consists of approximately 400K of low-resolution videos (e.g., 480p). The videos are natural videos with diverse lightning, motions, and scenarios.
2. Training Set 2 includes approximately 10K of high-resolution videos (e.g., 1080p). The videos are natural videos with diverse lightning, motions, and scenarios.

Training Procedure

Please refer to the paper and code for detailed training instructions.

Evaluation

Please refer to the paper and code for detailed evaluation instructions.

Intended Uses

We are sharing our model with the research community to foster further research in this area:

• Training your own video tokenizers for research purpose.
• Video tokenization with various compression rates.

Downstream Uses

Our model is designed to accelerate research on video-centric research, for use as a building block for the following applications:

• Video generation on the continuous / discrete latent tokens.
• World modelling on the continuous / discrete latent tokens.
• Generative games on the continuous / discrete latent tokens.
• Video understanding from the latent tokens.

Out-of-scope Uses

Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of video tokenizers (e.g., performance degradation on out-of-domain data) as they select use cases, and evaluate and mitigate for privacy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios.

Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.

Risks and Limitations

Some of the limitations of this model to be aware of include:

• VidTok may lose detailed information on the reconstructed content.
• VidTok inherits any biases, errors, or omissions characteristic of its training data.
• VidTok was developed for research and experimental purposes. Further testing and validation are needed before considering its application in commercial or real-world scenarios.

Recommendations

Some recommendations for alleviating potential limitations include:

• Lower compression rate provides higher reconstruction quality.
• For domain-specific video tokenization, it is suggested to fine-tune the model on the domain-specific videos.

License

The model is released under the MIT license.

Contact

We welcome feedback and collaboration from our audience. If you have suggestions, questions, or observe unexpected/offensive behavior in our technology, please contact us at [email protected].