  We propose {\it HumanDiffusion,} a diffusion model trained from humans'
perceptual gradients to learn an acceptable range of data for humans (i.e.,
human-acceptable distribution). Conventional HumanGAN aims to model the
human-acceptable distribution wider than the real-data distribution by training
a neural network-based generator with human-based discriminators. However,
HumanGAN training tends to converge in a meaningless distribution due to the
gradient vanishing or mode collapse and requires careful heuristics. In
contrast, our HumanDiffusion learns the human-acceptable distribution through
Langevin dynamics based on gradients of human perceptual evaluations. Our
training iterates a process to diffuse real data to cover a wider
human-acceptable distribution and can avoid the issues in the HumanGAN
training. The evaluation results demonstrate that our HumanDiffusion can
successfully represent the human-acceptable distribution without any heuristics
for the training.
