Make-A-Video: Text-to-Video Generation without Text-Video Data (2209.14792v1)

Abstract: We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video footage. Make-A-Video has three advantages: (1) it accelerates training of the T2V model (it does not need to learn visual and multimodal representations from scratch), (2) it does not require paired text-video data, and (3) the generated videos inherit the vastness (diversity in aesthetic, fantastical depictions, etc.) of today's image generation models. We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. First, we decompose the full temporal U-Net and attention tensors and approximate them in space and time. Second, we design a spatial temporal pipeline to generate high resolution and frame rate videos with a video decoder, interpolation model and two super resolution models that can enable various applications besides T2V. In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation, as determined by both qualitative and quantitative measures.

Make-A-Video: Text-to-Video Generation without Text-Video Data

The given paper, "Make-A-Video: Text-to-Video Generation without Text-Video Data," discusses a novel approach to advancing Text-to-Video (T2V) generation by leveraging the substantial advancements recently achieved in Text-to-Image (T2I) generation. This paper aims to overcome the significant barrier posed by the lack of large-scale, high-quality text-video paired datasets, a limitation that has historically impeded progress in T2V models. By using existing paired text-image data and leveraging unsupervised video data, the authors demonstrate impressive results in T2V generation without the necessity of paired text-video datasets.

Methodology and Contributions

The methodology introduced in the paper, termed Make-A-Video, integrates three main components:

1. Extending traditional T2I models to include temporal dynamics.
2. Incorporating spatial-temporal convolutional and attention layers.
3. Implementing a novel interpolation technique to enhance frame rate, fidelity, and temporal coherence of generated videos.

Make-A-Video builds upon existing T2I models, thereby circumventing the need for learning visual and multimodal representations from scratch. This approach involves leveraging the spatial knowledge inherent in T2I models and augmenting it with temporal dynamics derived from unlabeled video data. Specific advancements in Make-A-Video include:

• Spatiotemporal Modules: By expanding U-Net-based networks to include pseudo-3D convolutional and attention layers, the model can process and generate temporally coherent video sequences.
• Spatial-Temporal Resolution Enhancement: The model integrates spatial super-resolution networks and a frame interpolation network to generate high-definition and high frame-rate videos.
• Frame Interpolation Network: A novel network for frame interpolation and extrapolation augments the model’s ability to generate smooth and temporally coherent video sequences from a lower frame rate input.

Results and Evaluation

The authors present both qualitative and quantitative evaluations to assert the superiority of Make-A-Video over existing T2V methods. Key performance metrics include Frechet Inception Distance (FID), Frechet Video Distance (FVD), CLIP similarity (CLIPSIM), and human evaluation metrics focused on video quality and text-video faithfulness. Results demonstrated significant improvements:

• MSR-VTT Dataset: The model achieved a state-of-the-art FID of 13.17 and a CLIPSIM of 0.3049, outperforming previous models including GODIVA, NÜWA, and CogVideo.
• UCF-101: Make-A-Video attained an Inception Score (IS) of 33.00 and FVD of 367.23 in zero-shot settings, and further improved performance when fine-tuned.
• Human Evaluations: In head-to-head comparisons with CogVideo and VDM, Make-A-Video was preferred by human raters in terms of both quality and faithfulness, with a notable preference margin (e.g., 77.15% preferred it over CogVideo on quality metrics).

Implications and Future Directions

The implications of this research are multifold:

• Practical Applications: By bypassing the need for large-scale paired text-video datasets, the Make-A-Video model significantly lowers the barrier to entry in high-fidelity T2V generation. This makes it feasible to develop applications in entertainment, educational content creation, and digital marketing, where custom video generation from textual descriptions can be highly beneficial.
• Theoretical Advancements: The methodology exemplifies how unsupervised learning on massive unstructured data can be harnessed to extend the capabilities of structured model training. This paradigm could be extended further to other domains requiring multimodal learning.

Conclusion

Make-A-Video represents a substantial step forward in the domain of T2V generation by elegantly combining insights from both T2I models and unsupervised learning from video data. The elegance of this approach lies in its efficiency and scalability, achieving state-of-the-art results while maintaining reproducibility and transparency. Future work will likely focus on addressing the limitations related to longer video generation, more nuanced actions, and continuously managing the bias inherent in the training data.

The continued exploration of such hybrid models leveraging both supervised and unsupervised learning approaches suggests that the field of AI-generated content will witness even greater innovations, pushing the boundaries of what's possible in AI-driven creativity and content generation.