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VidTok: A Versatile and Open-Source Video Tokenizer

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arxiv 2412.13061 v1 pith:NRTEEOZE submitted 2024-12-17 cs.CV cs.AIcs.LG

Anni Tang , Tianyu He , Junliang Guo , Xinle Cheng , Li Song , Jiang Bian This is my paper
classification cs.CVcs.AIcs.LG
keywords videovidtoktrainingaddressadvancementsdiscreteexistingincluding
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

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