- AI-generated videos often lose consistency over time due to a problem called drift.
- Models trained on perfect data struggle to handle imperfect real-world inputs
- EPFL researchers have developed error recycling to limit progressive degradation
AI-generated videos often lose consistency as footage gets longer, a problem known as drift.
This problem occurs because each new image is generated based on the previous one, so any small errors, such as a distorted object or a slightly blurred face, are magnified over time.
Large language models trained exclusively on ideal datasets have difficulty handling imperfect input, which is why videos usually become unrealistic after a few seconds.
Recycle errors to improve AI performance
Generating videos that maintain logical continuity over extended periods remains a major challenge in the field.
Researchers at EPFL’s Visual Intelligence for Transportation (VITA) laboratory have introduced a method called error recycling.
Unlike conventional approaches that attempt to avoid errors, this method deliberately reincorporates the AI’s own errors into the training process.
In doing so, the model learns to correct errors in future images, thereby limiting progressive image degradation.
The process involves generating a video, identifying discrepancies between the produced frames and the intended frames, and retraining the AI on these discrepancies to refine future results.
Current AI video systems typically produce footage that remains realistic for less than 30 seconds before shapes, colors and motion logic deteriorate.
By incorporating error recycling, the EPFL team produced videos that resist drift over longer durations, potentially removing the strict temporal constraints on generative video.
This advancement allows AI systems to create more stable sequences in applications such as simulations, animation or automated visual storytelling.
Although this approach resolves drift, it does not eliminate all technical limitations.
Retraining by retraining errors increases computational demand and may require continuous monitoring to avoid overfitting to specific errors.
Deploying at scale may face resource and efficiency constraints, as well as the need to maintain consistency across diverse video content.
It remains unclear whether feeding the AI with its own errors is really a good idea, as the method could introduce unforeseen biases or reduce generalization in complex scenarios.
VITA Lab’s development shows that AI can learn from its own mistakes, potentially extending video generation times.
However, how this method will work outside of controlled testing or in creative applications remains unclear, suggesting caution before assuming that it can completely solve the drift problem.
Via TechXplore
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