VidTok: A Versatile and Open-Source Video Tokenizer
This addresses the need for high-performance, open-source video tokenizers in video generation and understanding research, representing an incremental advancement with specific gains.
The paper tackles the problem of encoding video content into compact latent tokens to reduce redundancy in pixel-level representations, introducing VidTok, a versatile video tokenizer that achieves state-of-the-art performance in both continuous and discrete tokenizations with improvements in metrics like PSNR, SSIM, LPIPS, and FVD.
Encoding video content into compact latent tokens has become a fundamental step in video generation and understanding, driven by the need to address the inherent redundancy in pixel-level representations. Consequently, there is a growing demand for high-performance, open-source video tokenizers as video-centric research gains prominence. We introduce VidTok, a versatile video tokenizer that delivers state-of-the-art performance in both continuous and discrete tokenizations. VidTok incorporates several key advancements over existing approaches: 1) model architecture such as convolutional layers and up/downsampling modules; 2) to address the training instability and codebook collapse commonly associated with conventional Vector Quantization (VQ), we integrate Finite Scalar Quantization (FSQ) into discrete video tokenization; 3) improved training strategies, including a two-stage training process and the use of reduced frame rates. By integrating these advancements, VidTok achieves substantial improvements over existing methods, demonstrating superior performance across multiple metrics, including PSNR, SSIM, LPIPS, and FVD, under standardized evaluation settings.