  This paper proposes the first non-flow-based deep framework for high dynamic
range (HDR) imaging of dynamic scenes with large-scale foreground motions. In
state-of-the-art deep HDR imaging, input images are first aligned using optical
flows before merging, which are still error-prone due to occlusion and large
motions. In stark contrast to flow-based methods, we formulate HDR imaging as
an image translation problem without optical flows. Moreover, our simple
translation network can automatically hallucinate plausible HDR details in the
presence of total occlusion, saturation and under-exposure, which are otherwise
almost impossible to recover by conventional optimization approaches. Our
framework can also be extended for different reference images. We performed
extensive qualitative and quantitative comparisons to show that our approach
produces excellent results where color artifacts and geometric distortions are
significantly reduced compared to existing state-of-the-art methods, and is
robust across various inputs, including images without radiometric calibration.
