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Photorealistic Video Generation with Diffusion Models (2312.06662v1)

Published 11 Dec 2023 in cs.CV, cs.AI, and cs.LG

Abstract: We present W.A.L.T, a transformer-based approach for photorealistic video generation via diffusion modeling. Our approach has two key design decisions. First, we use a causal encoder to jointly compress images and videos within a unified latent space, enabling training and generation across modalities. Second, for memory and training efficiency, we use a window attention architecture tailored for joint spatial and spatiotemporal generative modeling. Taken together these design decisions enable us to achieve state-of-the-art performance on established video (UCF-101 and Kinetics-600) and image (ImageNet) generation benchmarks without using classifier free guidance. Finally, we also train a cascade of three models for the task of text-to-video generation consisting of a base latent video diffusion model, and two video super-resolution diffusion models to generate videos of $512 \times 896$ resolution at $8$ frames per second.

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Citations (108)
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Summary

  • The paper presents W.A.L.T, which uses a causal encoder to compress both images and videos into a unified latent space for efficient cross-format training.
  • The paper introduces a novel window attention architecture that enhances memory efficiency and training, critical for high-res video super-resolution.
  • The paper demonstrates competitive performance by generating class-conditional, temporally coherent videos without relying on intensive classifier-free guidance.

Introduction to Photorealistic Video Generation

The field of AI-generated content has made significant strides, and a recent breakthrough showcases an advanced method for creating photorealistic videos from textual descriptions. This innovative approach leverages the power of diffusion models, which are a class of generative models that have gained traction for producing high-quality images. The model, known as W.A.L.T, utilizes a transformer-based architecture to accomplish this feat.

The Mechanics of W.A.L.T

The core innovation of W.A.L.T comprises two pivotal design choices: firstly, a causal encoder compresses both images and videos into a unified latent space, which enables the model to train across different formats efficiently. Secondly, a window attention architecture enhances memory and training efficiency, fundamental for handling the demanding task of video generation.

The unique pipeline involves a combination of three trained models. The process begins with a base latent video diffusion model, followed by two stages of video super-resolution models. These stages upscale the generated content to the desired high-resolution output, achieving impressive detail and temporal consistency in the resulting videos.

Performance and Training Efficiency

In benchmark tests, W.A.L.T has demonstrated remarkable results in generating class-conditional videos and has also exhibited significant aptitude on image generation benchmarks. Importantly, this has been achieved without the use of intensive computational techniques such as classifier-free guidance, signaling not just quality but also efficiency in the model's performance.

W.A.L.T's configurations allow for a harmonious balance between the number of model parameters and the quality of video generation. It showcases the importance of making strategic decisions on resource allocation within the model architecture to optimize both fidelity and computational load.

Innovating Beyond Still Images

While still imagery has seen considerable progress in generative modeling, video synthesis has lagged. The release of W.A.L.T is a notable push forward, demonstrating that high-resolution, temporally coherent videos can be generated effectively from textual descriptions. This opens avenues for a range of applications, from content creation to potential uses in virtual reality, simulations, and more.

W.A.L.T stands out for its ability to be jointly trained on both image and video datasets, which allows it to capitalize on the vast amount of image data available, in contrast to the sparser video datasets. The joint training methodology significantly benefits the model's performance, contributing to the more detailed and accurate video outputs.

Future Paths and Conclusion

W.A.L.T's success underscores the potential of scaling up the unified framework for image and video generation to close the existing gap between the two. The model's efficiency and the quality signal a new horizon in AI-driven content generation, where the boundaries of creativity and automation continue to expand, potentially transforming how visual media is produced and consumed in the future.

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