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Paper

X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation

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Bemana,  Mojtaba
Computer Graphics, MPI for Informatics, Max Planck Society;

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Myszkowski,  Karol       
Computer Graphics, MPI for Informatics, Max Planck Society;

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Seidel,  Hans-Peter       
Computer Graphics, MPI for Informatics, Max Planck Society;

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arXiv:2010.00450.pdf
(Preprint), 8MB

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Citation

Bemana, M., Myszkowski, K., Seidel, H.-P., & Ritschel, T. (2020). X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation. Retrieved from https://arxiv.org/abs/2010.00450.


Cite as: https://hdl.handle.net/21.11116/0000-0007-B6EC-2
Abstract
We suggest to represent an X-Field -a set of 2D images taken across different
view, time or illumination conditions, i.e., video, light field, reflectance
fields or combinations thereof-by learning a neural network (NN) to map their
view, time or light coordinates to 2D images. Executing this NN at new
coordinates results in joint view, time or light interpolation. The key idea to
make this workable is a NN that already knows the "basic tricks" of graphics
(lighting, 3D projection, occlusion) in a hard-coded and differentiable form.
The NN represents the input to that rendering as an implicit map, that for any
view, time, or light coordinate and for any pixel can quantify how it will move
if view, time or light coordinates change (Jacobian of pixel position with
respect to view, time, illumination, etc.). Our X-Field representation is
trained for one scene within minutes, leading to a compact set of trainable
parameters and hence real-time navigation in view, time and illumination.