At the TU Wien (Vienna), neural
networks have been developed which make it much easier to create photorealistic
pictures of a wide variety of materials. If computer-generated images are to
look realistic, different materials have to be presented differently: The
metallic sheen of a coin looks quite different from the dull gloss of a wooden
plate or the slightly transparent skin of a grape. Exactly simulating such
material effects usually requires a lot of experience and patience. Many
different parameters need to be adjusted carefully, then the computer takes a
while to calculate the corresponding image, and then the same procedure is
repeated, until the result is fully satisfactory. New methods have now been
developed that make this process much faster and easier.
AI recognizes the designer's
creative desires and autonomously proposes suitable sample images. A neural
network applies the selected material parameters to a sample object in real
time. For very different applications in the graphics area, this is a big step
forward, from game design and film animation to architectural visualization. In
order for the computer to learn how to display a specific material, different
versions of a sample object are displayed. A person clicks on the image which
looks closest to the desired result. After a few practice rounds, the
artificial intelligence has learned the physical properties of the desired material.
That way the system acquires parameters which can then be used to insert
objects of this material into any image, matching any specific lighting.
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