Towards Multi-Task Multi-Modal Models: A Video Generative Perspective

TL;DR

This paper presents a comprehensive approach to multi-task, multi-modal video generation and understanding, introducing novel tokenization, scalable models, and surpassing diffusion models in visual synthesis.

Contribution

It introduces a scalable multi-modal transformer, novel video tokenizers, and demonstrates language models surpassing diffusion models in visual synthesis, advancing multi-task video generation and understanding.

Findings

Language models outperform diffusion models in visual synthesis.

Video tokenizer outperforms industry-standard codecs.

Scalable models enable high-fidelity, multi-modal video generation.

Abstract

Advancements in language foundation models have primarily fueled the recent surge in artificial intelligence. In contrast, generative learning of non-textual modalities, especially videos, significantly trails behind language modeling. This thesis chronicles our endeavor to build multi-task models for generating videos and other modalities under diverse conditions, as well as for understanding and compression applications. Given the high dimensionality of visual data, we pursue concise and accurate latent representations. Our video-native spatial-temporal tokenizers preserve high fidelity. We unveil a novel approach to mapping bidirectionally between visual observation and interpretable lexical terms. Furthermore, our scalable visual token representation proves beneficial across generation, compression, and understanding tasks. This achievement marks the first instances of language models surpassing diffusion models in visual synthesis and a video tokenizer outperforming industry-standard codecs. Within these multi-modal latent spaces, we study the design of multi-task generative models. Our masked multi-task transformer excels at the quality, efficiency, and flexibility of video generation. We enable a frozen language model, trained solely on text, to generate visual content. Finally, we build a scalable generative multi-modal transformer trained from scratch, enabling the generation of videos containing high-fidelity motion with the corresponding audio given diverse conditions. Throughout the course, we have shown the effectiveness of integrating multiple tasks, crafting high-fidelity latent representation, and generating multiple modalities. This work suggests intriguing potential for future exploration in generating non-textual data and enabling real-time, interactive experiences across various media forms.