Learning Physical Dynamics for Object-centric Visual Prediction
Huilin Xu Tao Chen Feng Xu
The ability to model the underlying dynamics of visual scenes and reason
about the future is central to human intelligence. Many attempts have been made
to empower intelligent systems with such physical understanding and prediction
abilities. However, most existing methods focus on pixel-to-pixel prediction,
which suffers from heavy computational costs while lacking a deep understanding
of the physical dynamics behind videos. Recently, object-centric prediction
methods have emerged and attracted increasing interest. Inspired by it, this
paper proposes an unsupervised object-centric prediction model that makes
future predictions by learning visual dynamics between objects. Our model
consists of two modules, perceptual, and dynamic module. The perceptual module
is utilized to decompose images into several objects and synthesize images with
a set of object-centric representations. The dynamic module fuses contextual
information, takes environment-object and object-object interaction into
account, and predicts the future trajectory of objects. Extensive experiments
are conducted to validate the effectiveness of the proposed method. Both
quantitative and qualitative experimental results demonstrate that our model
generates higher visual quality and more physically reliable predictions
compared to the state-of-the-art methods.