  The X-ray flux provided by X-ray free-electron lasers and storage rings
offers new spatiotemporal possibilities to study in-situ and operando dynamics,
even using single pulses of such facilities. X-ray Multi-Projection Imaging
(XMPI) is a novel technique that enables volumetric information using single
pulses of such facilities and avoids centrifugal forces induced by
state-of-the-art time-resolved 3D methods such as time-resolved tomography. As
a result, XMPI can acquire 3D movies (4D) at least three orders of magnitude
faster than current methods. However, it is exceptionally challenging to
reconstruct 4D from highly sparse projections as acquired by XMPI with current
algorithms. Here, we present 4D-ONIX, a Deep Learning (DL)-based approach that
learns to reconstruct 3D movies (4D) from an extremely limited number of
projections. It combines the computational physical model of X-ray interaction
with matter and state-of-the-art DL methods. We demonstrate the potential of
4D-ONIX to generate high-quality 4D by generalizing over multiple experiments
with only two projections per timestamp for binary droplet collisions. We
envision that 4D-ONIX will become an enabling tool for 4D analysis, offering
new spatiotemporal resolutions to study processes not possible before.
