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We propose a method for compressively acquiring a dynamic light field (a 5-Dvolume) through a single-shot coded image (a 2-D measurement). We designed animaging model that synchronously applies aperture coding and pixel-wiseexposure coding within a single exposure time. This coding scheme enables us toeffectively embed the original information into a single observed image. Theobserved image is then fed to a convolutional neural network (CNN) forlight-field reconstruction, which is jointly trained with the camera-sidecoding patterns. We also developed a hardware prototype to capture a real 3-Dscene moving over time. We succeeded in acquiring a dynamic light field with5x5 viewpoints over 4 temporal sub-frames (100 views in total) from a singleobserved image. Repeating capture and reconstruction processes over time, wecan acquire a dynamic light field at 4x the frame rate of the camera. To ourknowledge, our method is the first to achieve a finer temporal resolution thanthe camera itself in compressive light-field acquisition. Our software isavailable from our project webpage

Acquiring a Dynamic Light Field through a Single-Shot Coded Image