Attention-Based Video Token Generation for High-Resolution Multimodal Output
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Solution Overview
Problem
Existing machine learning models struggle to generate high-quality videos with matching audio across various tasks efficiently, especially when dealing with diverse and imbalanced datasets, and require significant computational resources for high-resolution outputs.
Innovation Solution
An autoregressive token generation model using a decoder-only transformer architecture processes tokenized and embedded multimodal inputs to generate videos with audio, leveraging task adaptation and a unified vocabulary for diverse tasks, and employs a windowed-local attention mechanism for high-resolution outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing machine learning models are used to generate high-quality videos with matching audio, then video quality and audio matching can be achieved, but significant computational resources are required
Solution Approach 1:
The video generation process is divided into discrete token units that are generated autoregressively. The video is segmented into temporal tokens representing short video clips, which are then sequentially generated by the model. This segmentation allows the model to process and generate video content in manageable units, reducing the overall computational burden compared to generating entire high-resolution videos in one operation.
Solution Approach 2:
The patent introduces an intermediary token representation layer between the input conditions and the final video output. The model first generates intermediate video tokens that encode essential visual information, which then serve as a compressed representation before being decoded into final video frames. This intermediary representation reduces the direct computational complexity of generating high-resolution video pixels.
2Adaptability or versatility
If existing models handle diverse tasks, then task versatility is improved, but the models struggle with imbalanced datasets and require more resources
Solution Approach 1:
The patent implements a universal autoregressive video generation model that can perform multiple video generation tasks through a single unified architecture. The model accepts different input conditions (text prompts, image inputs, video inputs) and generates corresponding video outputs by conditioning on these inputs. This multi-functional design eliminates the need for separate specialized models for different video generation tasks, reducing overall computational resource requirements while maintaining task versatility.
3Manufacturing precision
If high-resolution video outputs are generated, then video quality is improved, but computational resources increase significantly
Solution Approach 1:
The video generation process is divided into discrete token units that are generated autoregressively. The video is segmented into temporal tokens representing short video clips, which are then sequentially generated by the model. This segmentation allows the model to process and generate video content in manageable units, reducing the overall computational burden compared to generating entire high-resolution videos in one operation.
Solution Approach 2:
The patent transitions the video generation problem from direct pixel-space manipulation to a latent token-space representation. By working in this compressed dimensional space where videos are represented as sequences of discrete tokens rather than full-resolution pixel grids, the model achieves high-resolution video generation with reduced computational complexity. The dimensionality reduction occurs naturally through the tokenization process.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a video output using an autoregressive token generation neural network model In one aspect, a system comprises obtaining a model input, processing the model input to generate an input sequence of embeddings that represents the model input, autoregressively generating a plurality of output sequences of tokens, wherein each output sequence of tokens corresponds to a respective output modality of tokens from a set of a plurality of modalities that includes a video modality and one or more other modalities, and generating a model output that includes a video output of the video modality by decoding the sequence of tokens.