  This paper proposes a human-aware deblurring model that disentangles the
motion blur between foreground (FG) humans and background (BG). The proposed
model is based on a triple-branch encoder-decoder architecture. The first two
branches are learned for sharpening FG humans and BG details, respectively;
while the third one produces global, harmonious results by comprehensively
fusing multi-scale deblurring information from the two domains. The proposed
model is further endowed with a supervised, human-aware attention mechanism in
an end-to-end fashion. It learns a soft mask that encodes FG human information
and explicitly drives the FG/BG decoder-branches to focus on their specific
domains. To further benefit the research towards Human-aware Image Deblurring,
we introduce a large-scale dataset, named HIDE, which consists of 8,422 blurry
and sharp image pairs with 65,784 densely annotated FG human bounding boxes.
HIDE is specifically built to span a broad range of scenes, human object sizes,
motion patterns, and background complexities. Extensive experiments on public
benchmarks and our dataset demonstrate that our model performs favorably
against the state-of-the-art motion deblurring methods, especially in capturing
semantic details.
