Scaling Autoregressive Video Models (1906.02634v3)

Abstract: Due to the statistical complexity of video, the high degree of inherent stochasticity, and the sheer amount of data, generating natural video remains a challenging task. State-of-the-art video generation models often attempt to address these issues by combining sometimes complex, usually video-specific neural network architectures, latent variable models, adversarial training and a range of other methods. Despite their often high complexity, these approaches still fall short of generating high quality video continuations outside of narrow domains and often struggle with fidelity. In contrast, we show that conceptually simple autoregressive video generation models based on a three-dimensional self-attention mechanism achieve competitive results across multiple metrics on popular benchmark datasets, for which they produce continuations of high fidelity and realism. We also present results from training our models on Kinetics, a large scale action recognition dataset comprised of YouTube videos exhibiting phenomena such as camera movement, complex object interactions and diverse human movement. While modeling these phenomena consistently remains elusive, we hope that our results, which include occasional realistic continuations encourage further research on comparatively complex, large scale datasets such as Kinetics.

Scaling Autoregressive Video Models: An Expert Overview

In the domain of video generative modeling, the paper titled "Scaling Autoregressive Video Models" by Dirk Weissenborn et al. introduces a novel approach to address the inherent complexities and vast data requirements associated with video generation. The authors propose a method that leverages autoregressive models alongside advancements in neural architectures, specifically focusing on transformer models extended for video data. This approach aims to achieve high fidelity and realistic video continuations across various benchmark datasets.

Core Contributions

The paper presents several key contributions to the field of video generative modeling:

1. Three-Dimensional Self-Attention Mechanism: The authors generalize the Transformer architecture using a three-dimensional, block-local self-attention mechanism. This modification enables efficient handling of videos as spatiotemporal volumes, as opposed to merely sequences of images, thus facilitating direct interactions between pixels across both spatial and temporal dimensions.
2. Spatiotemporal Subscaling: A novel subscaling technique is introduced, which segments videos into smaller slices based on subscale factors in time, height, and width. These slices are generated sequentially, drastically reducing memory requirements during training and potentially enhancing model scalability by allowing larger architectures.
3. Empirical Results on Benchmark Datasets: The paper demonstrates competitive results on datasets such as BAIR Robot Pushing and Kinetics-600. Particularly notable is the achieved reduction in perplexity on robot pushing datasets, suggesting enhanced model capability in capturing complex movements and interactions.

Technical Evaluation

The autoregressive model is trained and evaluated using both intrinsic and extrinsic metrics, including bits per dimension and Fréchet Video Distance (FVD). The empirical results underscore the model's superiority in generating realistic video sequences compared to previous approaches. The use of block-local self-attention allows the model to efficiently scale up, taking advantage of modern hardware accelerators such as TPUs.

Observations and Implications

The authors observe that while achieving state-of-the-art results, the simplicity and scalability of their model present unique opportunities and challenges:

• Fidelity and Realism: The autoregressive approach provides robust and high-fidelity results, particularly evident in the better FVD scores compared to existing models. However, certain phenomena such as occlusions and fast movements still pose challenges, indicating areas for future research improvements.
• Sampling Efficiency: Autoregressive models are traditionally slower during inference; however, the authors propose that advancements in parallel sampling strategies and hardware development could alleviate these issues.
• Complex Phenomena Modeling: Despite encouraging results, models struggle with the full-range complexities in datasets like Kinetics, highlighting the necessity for further research into capturing intricate real-world dynamics effectively.

Future Directions

The advancements presented in this paper pave the way for several intriguing research directions:

• Hardware and Algorithm Improvements: Enhancing parallelization techniques for autoregressive models and leveraging emerging hardware solutions could further optimize sampling efficiency and expand the applicability of these models in high-dimensional tasks.
• Handling Greater Complexity: Opportunities exist to refine models to address phenomena like fast movements and spatial occlusions more effectively, potentially through novel architectural modifications or training strategies.
• Cross-Modal Applications: The principles guiding video generation could be extended to other domains, such as multi-modal generative tasks that incorporate additional sensory data, thereby broadening the impact of autoregressive techniques.

The paper represents a significant step in the field of video generative modeling, offering a robust and scalable method that opens new avenues for research and application in dynamic and data-heavy environments.