Generating Videos with Scene Dynamics (1609.02612v3)

Abstract: We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene's foreground from the background. Experiments suggest this model can generate tiny videos up to a second at full frame rate better than simple baselines, and we show its utility at predicting plausible futures of static images. Moreover, experiments and visualizations show the model internally learns useful features for recognizing actions with minimal supervision, suggesting scene dynamics are a promising signal for representation learning. We believe generative video models can impact many applications in video understanding and simulation.

Generating Videos with Scene Dynamics

The paper "Generating Videos with Scene Dynamics" by Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba introduces a novel approach for leveraging large volumes of unlabeled video data to model scene dynamics for both video recognition and generation tasks. The authors present a generative adversarial network (GAN) designed specifically for video, with a spatio-temporal convolutional architecture that disentangles foreground and background components. This methodological innovation enables the generation of short videos with realistic dynamics and holds potential for improving action classification by learning useful features from video data with minimal supervision.

Introduction and Background

Understanding the transformation of scenes over time is a foundational issue in computer vision, crucial for tasks such as action classification and future prediction of video frames. The challenges in modeling scene dynamics stem from the complexity of capturing the myriad ways objects and scenes evolve. The paper addresses these challenges by utilizing large-scale, unlabeled video data, which is readily available and intrinsically contains rich temporal signals. This approach is economically feasible due to the inherent temporal coherence within video frames.

Generative Adversarial Network for Video

The authors propose a two-stream generative model that separates foreground from background, facilitating the learning process by enforcing a stationary background. This structure capitalizes on spatio-temporal convolutions and recent advances in GANs, extending them to video. The network comprises two main components:

1. Generator Network: The generator network processes a latent code $z$ to produce a video. It employs fractionally strided convolutions for upsampling, and a two-stream architecture to model foreground and background separately. The foreground stream uses spatio-temporal convolutions, while the background stream uses spatial convolutions replicated across the temporal axis.
2. Discriminator Network: The discriminator network distinguishes between real and generated videos, ensuring the generator produces realistic video frames and motion patterns. It employs spatio-temporal convolutions to capture both spatial and temporal invariances.

Experimental Evaluation

The paper showcases experiments across two main dimensions: video generation quality and the utility of learned video representations for action classification.

Video Generation

The authors conduct both qualitative and quantitative evaluations of the generated videos. Qualitatively, they note that the generated videos exhibit realistic scene dynamics, with the two-stream model effectively disentangling foreground motion from static backgrounds. Quantitatively, a psychophysical paper on Amazon Mechanical Turk demonstrates that humans prefer the GAN-generated videos over simple baseline methods, such as autoencoders. This preference is particularly pronounced for the two-stream architecture, which outperforms the one-stream variant in maintaining background stability and generating plausible motions.

Action Classification

The learned representations from the discriminator network are assessed on the temporal action recognition dataset UCF101. Fine-tuning the network on this task yields improvements over randomly initialized networks and hand-crafted features, such as STIP. This suggests that the model captures valuable dynamics pertinent to action recognition. Notably, the model shows significant performance gains in low-data regimes, underscoring the potential of unsupervised learning from unlabeled videos for representation learning.

Future Generation from Static Images

An intriguing application explored in the paper is the generation of plausible future videos from static images. By modifying the generator to be conditional on an input frame, the authors show that the network can extrapolate a sequence of future frames. Although the generated sequences are not always accurate, they exhibit reasonable and plausible dynamics, highlighting a promising direction for future research in predictive modeling of video data.

Implications and Future Directions

The research presented in this paper has both practical and theoretical implications. Practically, the ability to generate realistic video dynamics from unlabeled data can impact applications in video simulation, forecasting, and representation learning. Theoretically, the disentangling of foreground and background components in generative models provides insights into capturing the essential elements of scene dynamics.

Future research may focus on enhancing the resolution and accuracy of the generated videos, integrating more complex motion patterns, and extending the models to longer video sequences. Additionally, advancements in unsupervised learning techniques could further improve the learned representations, making them even more useful for downstream tasks such as video classification and activity recognition.

In conclusion, the paper presents a robust framework for learning and generating scene dynamics from unlabeled video data, showing promise for a range of applications in computer vision. The dual contributions of improved video generation and useful unsupervised features highlight the potential of generative video models to advance the state of the art in visual understanding.