Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks
This addresses video prediction for applications like visual analogy-making and extrapolation, but is incremental as it builds on existing probabilistic modeling approaches.
The paper tackles the problem of generating multiple likely future video frames from a single input image by modeling them probabilistically, achieving realistic results on synthetic and real-world data.
We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods that have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network; this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, and on real-world video frames. We present analyses of the learned network representations, showing it is implicitly learning a compact encoding of object appearance and motion. We also demonstrate a few of its applications, including visual analogy-making and video extrapolation.