DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
Abstract
Objects moving at high speed appear significantly
blurred when captured with cameras. The blurry appearance is especially ambiguous when the object has complex
shape or texture. In such cases, classical methods, or even
humans, are unable to recover the object’s appearance and
motion. We propose a method that, given a single image
with its estimated background, outputs the object’s appearance and position in a series of sub-frames as if captured by
a high-speed camera (i.e. temporal super-resolution). The
proposed generative model embeds an image of the blurred
object into a latent space representation, disentangles the
background, and renders the sharp appearance. Inspired by
the image formation model, we design novel self-supervised
loss function terms that boost performance and show good
generalization capabilities. The proposed DeFMO method
is trained on a complex synthetic dataset, yet it performs
well on real-world data from several datasets. DeFMO outperforms the state of the art and generates high-quality temporal super-resolution frames.