Posted on X by Karan Dalal Today, we're releasing a new paper – One-Minute Video Generation with Test-Time Training.
We add TTT layers to a pre-trained Transformer and fine-tune it to generate one-minute Tom and Jerry cartoons with strong temporal consistency.
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Research Notes: One-Minute Video Generation with Test-Time Training
Overview
The paper introduces a novel method for generating one-minute-long animated videos using Test-Time Training (TTT). By incorporating TTT layers into a pre-trained Transformer model, the researchers achieved high temporal consistency in video generation. The approach focuses on producing short-form animations, such as Tom and Jerry cartoons, directly from the model without extensive fine-tuning. This advancement addresses the challenge of maintaining coherence over longer video sequences [Result 1].
Technical Analysis
The core innovation lies in integrating TTT layers into a pre-trained Transformer architecture. These layers enable the model to adapt to specific tasks during inference, allowing for real-time adjustments and improved temporal consistency. The researchers leveraged a dataset of short video clips, likely sourced from platforms like YouTube, to fine-tune the model [Result 3].
The method builds upon recent advancements in language models, such as LLaMA, by extending their capabilities to handle sequential data. Unlike traditional fine-tuning approaches that require retraining on large datasets, TTT allows for incremental updates during inference, making the process more efficient and scalable [Result 2].
The implementation employs a two-stage process: pre-training on a general video dataset and then applying TTT during the generation phase to adapt to specific content (e.g., Tom and Jerry). This approach ensures that the model maintains high-quality output while reducing computational overhead [Result 4].
Implementation Details
- Model Architecture: The researchers used a Transformer-based architecture pre-trained on large-scale video data. TTT layers were added to enable dynamic adjustments during inference.
- Training Data: A combination of general video datasets and domain-specific content (e.g., cartoons) was used for fine-tuning.
- Evaluation Metrics: The model's performance was evaluated using metrics such as temporal consistency, visual quality, and user preference studies on a test set [Result 5].
The code for this project is available on GitHub, with the implementation details and examples provided in the repository [Result 4].
Related Technologies
- Language Models: The TTT approach draws inspiration from recent work on language models like LLaMA, which use similar techniques for fine-tuning during inference [Result 2].
- Diffusion Models: While not directly comparable, diffusion models have been used in video generation tasks, and the temporal consistency achieved here could be complementary to such approaches.
- Video Generation: Previous work on video GANs (Generative Adversarial Networks) focused on shorter sequences, but this paper extends these methods to one-minute videos with improved stability [Result 1].
Key Takeaways
- The introduction of TTT layers significantly improves temporal consistency in video generation tasks [Result 1].
- The method demonstrates the potential of adapting language model techniques (e.g., LLaMA) for video generation [Result 2].
- The use of fine-tuning during inference reduces computational overhead and enables real-time adjustments to generated content [Result 4].
Further Research
Here's a well-organized 'Further Reading' section based on the provided search results:
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One-Minute Video Generation with Test-Time Training
- arXiv
- Additional resources include PDFs available on arXiv and CVF Open Access.
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Project Website
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Research Paper (PDF)
- Available via CVF Open Access
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GitHub Repository
This section consolidates the unique sources while noting that some are duplicates or supplementary materials.