  We propose a method for compressively acquiring a dynamic light field (a 5-D
volume) through a single-shot coded image (a 2-D measurement). We designed an
imaging model that synchronously applies aperture coding and pixel-wise
exposure coding within a single exposure time. This coding scheme enables us to
effectively embed the original information into a single observed image. The
observed image is then fed to a convolutional neural network (CNN) for
light-field reconstruction, which is jointly trained with the camera-side
coding patterns. We also developed a hardware prototype to capture a real 3-D
scene moving over time. We succeeded in acquiring a dynamic light field with
5x5 viewpoints over 4 temporal sub-frames (100 views in total) from a single
observed image. Repeating capture and reconstruction processes over time, we
can acquire a dynamic light field at 4x the frame rate of the camera. To our
knowledge, our method is the first to achieve a finer temporal resolution than
the camera itself in compressive light-field acquisition. Our software is
available from our project webpage
