Learning to Decompose and Disentangle Representations for Video Prediction (1806.04166v2)

Abstract: Our goal is to predict future video frames given a sequence of input frames. Despite large amounts of video data, this remains a challenging task because of the high-dimensionality of video frames. We address this challenge by proposing the Decompositional Disentangled Predictive Auto-Encoder (DDPAE), a framework that combines structured probabilistic models and deep networks to automatically (i) decompose the high-dimensional video that we aim to predict into components, and (ii) disentangle each component to have low-dimensional temporal dynamics that are easier to predict. Crucially, with an appropriately specified generative model of video frames, our DDPAE is able to learn both the latent decomposition and disentanglement without explicit supervision. For the Moving MNIST dataset, we show that DDPAE is able to recover the underlying components (individual digits) and disentanglement (appearance and location) as we would intuitively do. We further demonstrate that DDPAE can be applied to the Bouncing Balls dataset involving complex interactions between multiple objects to predict the video frame directly from the pixels and recover physical states without explicit supervision.

• The paper introduces the DDPAE model that decomposes video frames into low-dimensional, disentangled components to simplify future frame prediction.
• The methodology leverages a variational auto-encoder framework to separate content and pose factors, achieving lower mean squared error on datasets like Moving MNIST and Bouncing Balls.
• The framework holds promise for real-world applications in robotics and surveillance, advancing unsupervised learning for video predictive modeling.

Learning to Decompose and Disentangle Representations for Video Prediction

The paper "Learning to Decompose and Disentangle Representations for Video Prediction" introduces a novel framework that tackles the challenge of predicting future video frames by decomposing high-dimensional video data into lower-dimensional components. Named the Decompositional Disentangled Predictive Auto-Encoder (DDPAE), this framework uniquely combines structured probabilistic models and deep networks to enhance the prediction accuracy in video sequences.

The central hypothesis of the DDPAE framework is that video prediction tasks can be simplified by breaking down a video into components and disentangling these into factors with low-dimensional temporal dynamics. The authors contend that learning these decompositions and disentanglements automatically, without explicit supervision, significantly reduces the complexity inherent in high-dimensional video frame prediction tasks. This hypothesis is substantiated through experiments on datasets where the underlying motions can be more simply understood, such as Moving MNIST and Bouncing Balls.

Methodology

The methodology employed involves a variational auto-encoder-based approach that integrates decomposition and disentanglement. The decomposition aspect relates to the separation of video frames into distinct components that are easier to predict individually. The disentanglement refers to representing each component with time-invariant content and low-dimensional temporal pose vectors. Thus, the prediction of the entire frame translates into predicting only the dynamics of pose vectors, which is less complex than predicting the high-dimensional video frames.

Results and Analysis

The authors evaluate the DDPAE model on two datasets known for video prediction challenges. For Moving MNIST, the model achieves superior results compared to state-of-the-art methods by effectively learning to separate and predict individual moving digits. Quantitatively, the DDPAE framework yields a notably lower Mean Squared Error (MSE) of 38.9 compared to other methods, reflecting significant improvements in generative precision and prediction capabilities. Qualitative results also demonstrate the model's capability to correctly handle overlapping digits, maintaining distinct component predictions.

Similarly, in the Bouncing Balls dataset—characterized by complex physical interactions—the model demonstrates robust prediction of motion and object collision dynamics, even without explicit modeling of physical states. The prediction relies on the inherent ability of the model to learn from pixel data, thus showcasing an advancement over existing methods that typically require knowledge of physical states for prediction.

Implications and Future Directions

The work presented in this paper has several theoretical and practical implications. Theoretically, it advances the field of unsupervised learning by illustrating that complex tasks such as video prediction can be effectively addressed through decomposition and disentanglement in latent space representations. Practically, the ability to predict future video frames has applications in numerous domains including robotics, surveillance, and interactive gaming.

Future research directions may involve extending the ideas of decomposition and disentanglement to more complex real-world datasets, possibly integrating reinforcement learning to enhance adaptive prediction in dynamic environments. The approach's scalability and ability to generalize beyond constrained datasets remain an open question, prompting further exploration into architectures that can handle increased complexity and variability in video sequences. Additionally, integrating this framework with other generative models could potentially improve the realism and quality of predicted frames, aligning more closely with actual future observations.

In conclusion, the DDPAE model contributes a substantive advancement in video prediction by effectively learning to parse high-dimensional data into simpler, more predictable structures, and heralds promising avenues for further investigation in AI-driven predictive modeling.