Predicting Video with VQVAE (2103.01950v1)

Abstract: In recent years, the task of video prediction-forecasting future video given past video frames-has attracted attention in the research community. In this paper we propose a novel approach to this problem with Vector Quantized Variational AutoEncoders (VQ-VAE). With VQ-VAE we compress high-resolution videos into a hierarchical set of multi-scale discrete latent variables. Compared to pixels, this compressed latent space has dramatically reduced dimensionality, allowing us to apply scalable autoregressive generative models to predict video. In contrast to previous work that has largely emphasized highly constrained datasets, we focus on very diverse, large-scale datasets such as Kinetics-600. We predict video at a higher resolution on unconstrained videos, 256x256, than any other previous method to our knowledge. We further validate our approach against prior work via a crowdsourced human evaluation.

Predicting Video with VQVAE: A Technical Synopsis

In the paper "Predicting Video with VQVAE," the authors present a sophisticated approach for video prediction by leveraging Vector Quantized Variational AutoEncoders (VQ-VAE). Video prediction, which involves forecasting future video frames given past frames, is a complex task due to the high-dimensional nature of video data. This work proposes a novel method to compress high-resolution video data into discrete latent variables, allowing for efficient and scalable prediction of future frames through autoregressive models. The paper focuses particularly on unconstrained video datasets, such as Kinetics-600, and demonstrates predictions at a resolution of 256x256, surpassing prior methods.

Key Contributions

1. Novel Application of VQ-VAE: The authors extend the usage of VQ-VAE architecture to video data, achieving substantial compression. This reduces the dimensionality significantly—by more than 98% compared to representing videos at the pixel level—thus facilitating tractable modeling.
2. Spatiotemporal PixelCNNs: The paper proposes using PixelCNN augmented with spatiotemporal self-attention and causal convolutions to work with the discrete latent representation acquired from VQ-VAE. This approach addresses issues such as mode-collapse and training instability frequently associated with GAN-based methods.
3. High-Resolution Video Prediction: The approach not only predicts video at higher resolutions but also validates performance through human evaluations, indicating strong preference for the VQ-VAE model's predictions over prior models.
4. Hierarchical Latent Representation: The authors employ a hierarchical decomposition of latent variables separating global information from fine details, allowing for specialized autoregressive models at different hierarchy levels.

Experimental Evaluations

Quantitative results using Fréchet Video Distance (FVD) reflect competitive performance—though not necessarily surpassing GAN approaches. Notably, human evaluations show a preference for the VQ-VAE-generated samples compared to samples from state-of-the-art GAN models, despite these models exhibiting lower FVD scores. This discrepancy suggests potential biases in automated metrics favoring GANs due to their training on classifier-based losses.

Implications and Future Directions

The implications of this work are wide-ranging, relevant for areas such as video interpolation, anomaly detection, and activity understanding in computer vision, and extending into robotics and reinforcement learning. The compression and predictive capacity provided by VQ-VAE could lead to advancements in creating efficient models for autonomous systems equipped to anticipate environmental dynamics.

The approach highlights the importance of latent space modeling for scalable video prediction, which could influence future developments in generative models beyond GANs. This methodology paves the way for more refined, likelihood-based models in video prediction, potentially offering greater diversity and stability compared to current GAN-based solutions.

Broader Impact and Ethical Considerations

The capabilities of video prediction raise significant ethical considerations, particularly regarding privacy and misinformation. The potential to use generative models for deceptive or malicious ends necessitates ongoing advancements in detection tools for computer-generated media. Furthermore, this paper emphasizes best practices in ethical research dissemination by advocating for use of publicly licensed videos for demonstration purposes.

In summary, the paper "Predicting Video with VQVAE" makes substantial strides in compressive video modeling and prediction, presenting a method that is both scalable and robust, with promising implications for diverse applications in artificial intelligence.