A Recipe for Scaling up Text-to-Video Generation with Text-free Videos
(2312.15770)Abstract
Diffusion-based text-to-video generation has witnessed impressive progress in the past year yet still falls behind text-to-image generation. One of the key reasons is the limited scale of publicly available data (e.g., 10M video-text pairs in WebVid10M vs. 5B image-text pairs in LAION), considering the high cost of video captioning. Instead, it could be far easier to collect unlabeled clips from video platforms like YouTube. Motivated by this, we come up with a novel text-to-video generation framework, termed TF-T2V, which can directly learn with text-free videos. The rationale behind is to separate the process of text decoding from that of temporal modeling. To this end, we employ a content branch and a motion branch, which are jointly optimized with weights shared. Following such a pipeline, we study the effect of doubling the scale of training set (i.e., video-only WebVid10M) with some randomly collected text-free videos and are encouraged to observe the performance improvement (FID from 9.67 to 8.19 and FVD from 484 to 441), demonstrating the scalability of our approach. We also find that our model could enjoy sustainable performance gain (FID from 8.19 to 7.64 and FVD from 441 to 366) after reintroducing some text labels for training. Finally, we validate the effectiveness and generalizability of our ideology on both native text-to-video generation and compositional video synthesis paradigms. Code and models will be publicly available at https://tf-t2v.github.io/.
Overview
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The paper introduces a novel text-to-video generation framework called TF-T2V, which mitigates the need for large-scale text-annotated video datasets.
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TF-T2V operates by using unlabelled videos and image-text data to learn video synthesis and spatial appearance generation separately.
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The approach demonstrates improved text-to-video generation quality, as shown by lower FID and FVD scores.
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The model is versatile and can handle different types of video synthesis tasks while proving to be scalable.
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Challenges remain in scaling the datasets, generating longer videos, and improving the model to understand complex actions.
Understanding Text-to-Video Generation
Introduction
Creating videos from textual descriptions is a significant challenge in artificial intelligence, particularly due to the complexity of videos, which involve both visual content and temporal dynamics. Advances in generative models have made significant strides in this domain, yet text-to-video generation still largely lags behind image generation. A crucial factor limiting progress is the scarcity of large-scale text-annotated video datasets, as video captioning is resource-intensive. Consequently, existing datasets pale in comparison to the vast amount of image-text pairs available, such as the billions contained in LAION's databases.
A Novel Approach
Researchers have proposed a framework known as TF-T2V (Text-Free Text-to-Video), which leverages the abundance of unlabelled videos readily available from sources like YouTube, thus bypassing the need for text-video pairs entirely. By decoupling the textual decoding process from temporal modeling, two branches are trained: one for content generation and the other for motion dynamics, sharing weights for optimization. The content branch uses image-text data to learn spatial appearance generation while the motion branch learns video synthesis from the text-free videos, capturing intricate motion patterns.
Scalability and Performance
The study showcases that expanding the training set with text-free videos can yield improvements in performance, as demonstrated by lower FID (Frechet Inception Distance) and FVD (Frechet Video Distance) scores, which are metrics for evaluating video quality and temporal coherence. Additionally, reintroducing text labels can further enhance performance, suggesting a sustainable model that scales up effectively with more data. The framework's versatility is proven across different tasks, such as native text-to-video generation and compositional video synthesis, which includes additional controls like depth, sketch, and motion vectors.
Implementation Insights
The paper details the underlying structure of the TF-T2V model, built upon available baselines and showcasing its applicability in high-definition video generation. Through quantitative measures, user studies, and ablation tests, the effectiveness of the proposed methods is confirmed. The temporal coherence loss in particular bolsters the production of smoothly transitioning videos.
Limitations and Future Directions
As with any research, there are avenues for further exploration. One limitation cited is the unexplored potential of scaling with text-free video datasets significantly larger than the ones used. Another is the potential for processing longer-form videos, which remains a challenge within the current scope of this study. Additionally, more refinement is needed for the model to precisely interpret and render videos that require understanding complex action descriptions embedded in text prompts.
Conclusion
This development in text-to-video generation illustrates a significant step forward in the field's pursuit to create realistic and temporally coherent videos from text. The research indicates that scalable and versatile video generation is feasible without relying on extensive text annotations, opening up new possibilities for content creation using advanced AI techniques. With the code and models slated for public release, the work promises to contribute significantly to future advances in video generation technology.
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