Text-to-Video Generation via Text-Image Pairs and Super-Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current text-video generation models are limited by the lack of large-scale high-quality text-video datasets and the complexity of modeling higher-dimensional video data, leading to restricted domains and increased costs, as they require paired text-video data for training.
Innovation Solution
A text-video generation framework that leverages open-source text-image datasets and unsupervised learning to learn from unpaired video data, using pseudo-3D convolutional layers and temporal attention modules to generate videos from text without relying on paired data, and employs super-resolution models to enhance video quality and frame rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If text-video generation models are trained on large-scale paired text-video datasets, then video generation quality and fidelity improve, but data availability and training cost worsen due to lack of such datasets
Solution Approach 1:
The patent introduces an intermediary approach by using text-image paired datasets as a bridge. Instead of directly training on scarce text-video pairs, the model first learns from abundant text-image pairs, then adapts to video generation through temporal frame sequences. This intermediary training stage leverages the availability of text-image data to bootstrap video generation capabilities.
Solution Approach 2:
The patent transforms the 2D image generation problem into a 3D video generation problem by adding the temporal dimension. The model generates multiple frames from a single text prompt, effectively extending the generation task from spatial (width x height) to spatiotemporal (frames x width x height), thereby utilizing video's temporal特性 without requiring video-specific training data.
2Ease of manufacture
If standard video generation models are trained on smaller private text-video pairs, then training cost and data requirements reduce, but model adaptability and video quality deteriorate
Solution Approach 1:
The patent makes the model universal by enabling it to learn from multiple data types (images and videos) for a single function (video generation). The model is designed to accept both image and video inputs during training, making it adaptable to different data formats and domains, thereby improving versatility without increasing data requirements.
Solution Approach 2:
The patent applies preliminary training on text-image paired datasets before fine-tuning on video data. This preliminary action allows the model to learn fundamental generation capabilities from abundant image data, which then serves as a foundation for subsequent video generation tasks, reducing the need for extensive video-specific training data.
3Reliability
If paired text-video data is required for training, then training data quality improves, but data acquisition difficulty and processing complexity increase
Solution Approach 1:
The patent uses image copies as video frames. By generating multiple copies of the same image with temporal variations, the model creates video sequences from static image generation, effectively bypassing the need for complex video data collection and pairing while maintaining generation quality.
4Manufacturing precision
If high-resolution video generation is implemented, then output quality improves, but computational resources and processing time increase
Solution Approach 1:
The patent segments the video generation process into distinct stages: text-to-image generation, frame sequence generation, and temporal interpolation. By dividing the high-resolution video generation into these manageable segments, the computational burden is distributed across multiple smaller processing tasks rather than requiring all resources simultaneously for the entire video.
Data Source
AI summary
A method and system for text-to-video generation. The method includes receiving a text input, generating a representation frame based on the text input using a model trained on text-image pairs, generating a set of frames based on the representation frame and a first frame rate, interpolating the set of frames to a higher frame rate, generating a first video based on the interpolated set of frames, increasing a resolution of the first video based on a first and second super-resolution model, and generating an output video based on a result of the super-resolution models.