654 research outputs found
MOGAN: Morphologic-structure-aware Generative Learning from a Single Image
In most interactive image generation tasks, given regions of interest (ROI)
by users, the generated results are expected to have adequate diversities in
appearance while maintaining correct and reasonable structures in original
images. Such tasks become more challenging if only limited data is available.
Recently proposed generative models complete training based on only one image.
They pay much attention to the monolithic feature of the sample while ignoring
the actual semantic information of different objects inside the sample. As a
result, for ROI-based generation tasks, they may produce inappropriate samples
with excessive randomicity and without maintaining the related objects' correct
structures. To address this issue, this work introduces a
MOrphologic-structure-aware Generative Adversarial Network named MOGAN that
produces random samples with diverse appearances and reliable structures based
on only one image. For training for ROI, we propose to utilize the data coming
from the original image being augmented and bring in a novel module to
transform such augmented data into knowledge containing both structures and
appearances, thus enhancing the model's comprehension of the sample. To learn
the rest areas other than ROI, we employ binary masks to ensure the generation
isolated from ROI. Finally, we set parallel and hierarchical branches of the
mentioned learning process. Compared with other single image GAN schemes, our
approach focuses on internal features including the maintenance of rational
structures and variation on appearance. Experiments confirm a better capacity
of our model on ROI-based image generation tasks than its competitive peers
3D GANs and Latent Space: A comprehensive survey
Generative Adversarial Networks (GANs) have emerged as a significant player
in generative modeling by mapping lower-dimensional random noise to
higher-dimensional spaces. These networks have been used to generate
high-resolution images and 3D objects. The efficient modeling of 3D objects and
human faces is crucial in the development process of 3D graphical environments
such as games or simulations. 3D GANs are a new type of generative model used
for 3D reconstruction, point cloud reconstruction, and 3D semantic scene
completion. The choice of distribution for noise is critical as it represents
the latent space. Understanding a GAN's latent space is essential for
fine-tuning the generated samples, as demonstrated by the morphing of
semantically meaningful parts of images. In this work, we explore the latent
space and 3D GANs, examine several GAN variants and training methods to gain
insights into improving 3D GAN training, and suggest potential future
directions for further research
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