Video Generation Model Transfer for Low-Label Photorealistic Training
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Solution Overview
Problem
Existing AI-based video generation models require large amounts of accurate labeled data, which is expensive, time-consuming, and prone to inconsistency, making it difficult to train models effectively with limited data.
Innovation Solution
A method involving training a video generation model by leveraging a first image generation model for a virtual domain style and a second image generation model for a real-world domain style, using a pre-trained video generation model with spatial and temporal attention layers, and employing a label interpolation model to enhance frame rate, allowing generation of photorealistic videos with a small amount of labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to create accurate labeled data, then data accuracy is improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent uses synthetic data generated by image generation models to create labeled video data. Instead of manually labeling real videos, the system generates synthetic video frames and their corresponding labels through AI models, copying the labeling function to automated synthesis processes. This resolves the contradiction by providing accurate labels without manual time investment.
Solution Approach 2:
The system employs self-supervised learning where the model generates its own training data and labels. The image generation model creates synthetic video frames and automatically provides ground truth labels, allowing the system to serve its own labeling needs without external human annotators. This eliminates both cost and time constraints of manual labeling.
2Productivity
If auto labeling is used to reduce cost and time, then efficiency is improved, but labeling accuracy deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where synthetic labels generated by the image generation model are used to train the video generation model. The system continuously refines its labeling through iterative training processes, where the model learns from synthetic data and improves its labeling accuracy over time. This feedback loop resolves the accuracy-efficiency contradiction by automating labeling while maintaining quality through continuous learning.
Solution Approach 2:
The patent introduces an intermediary synthetic data generation layer between the input video and the training labels. Instead of directly labeling real videos (manual) or using imperfect auto-labels, the system uses a synthetic data generator as an intermediary that creates intermediate representations with ground truth labels. This intermediary layer enables both efficiency and accuracy by bridging the gap between raw data and training labels.
3Manufacturing precision
If photorealistic video generation is achieved using real-labeled data, then video quality is improved, but data collection difficulty increases
Solution Approach 1:
The patent copies real video data into synthetic video data generated by image generation models. Instead of collecting and labeling real videos, the system generates synthetic versions that replicate the visual characteristics and temporal patterns of real videos. This copying approach maintains video generation quality while eliminating the difficulty of collecting and annotating real-world data.
Solution Approach 2:
The system transforms real video parameters into synthetic parameters through the image generation model. By changing the data generation parameters from real camera captures to AI-synthesized frames, the system preserves the essential visual and temporal characteristics needed for high-quality video generation while avoiding the data collection challenges of real-world footage. The parameter transformation enables quality maintenance without direct real data dependency.
Data Source
AI summary
A video generation model training method includes obtaining first time-series label data and time-series images of a first domain style, training a first image generation model based on the first time-series label data and the time-series images of the first domain style, obtaining a plurality of label data sets and a plurality of images of a second domain style, training a second image generation model based on the plurality of label data sets and the plurality of images of the second domain style, training a first video generation model based on the first image generation model, the first time-series label data, and the time-series images of the first domain style, and generating a second video generation model associated with the second domain style based on the second image generation model and the first video generation model.