  Existing image generator networks rely heavily on spatial convolutions and,
optionally, self-attention blocks in order to gradually synthesize images in a
coarse-to-fine manner. Here, we present a new architecture for image
generators, where the color value at each pixel is computed independently given
the value of a random latent vector and the coordinate of that pixel. No
spatial convolutions or similar operations that propagate information across
pixels are involved during the synthesis. We analyze the modeling capabilities
of such generators when trained in an adversarial fashion, and observe the new
generators to achieve similar generation quality to state-of-the-art
convolutional generators. We also investigate several interesting properties
unique to the new architecture.
