5,392 research outputs found
Diversified Texture Synthesis with Feed-forward Networks
Recent progresses on deep discriminative and generative modeling have shown
promising results on texture synthesis. However, existing feed-forward based
methods trade off generality for efficiency, which suffer from many issues,
such as shortage of generality (i.e., build one network per texture), lack of
diversity (i.e., always produce visually identical output) and suboptimality
(i.e., generate less satisfying visual effects). In this work, we focus on
solving these issues for improved texture synthesis. We propose a deep
generative feed-forward network which enables efficient synthesis of multiple
textures within one single network and meaningful interpolation between them.
Meanwhile, a suite of important techniques are introduced to achieve better
convergence and diversity. With extensive experiments, we demonstrate the
effectiveness of the proposed model and techniques for synthesizing a large
number of textures and show its applications with the stylization.Comment: accepted by CVPR201
CanvasGAN: A simple baseline for text to image generation by incrementally patching a canvas
We propose a new recurrent generative model for generating images from text
captions while attending on specific parts of text captions. Our model creates
images by incrementally adding patches on a "canvas" while attending on words
from text caption at each timestep. Finally, the canvas is passed through an
upscaling network to generate images. We also introduce a new method for
generating visual-semantic sentence embeddings based on self-attention over
text. We compare our model's generated images with those generated Reed et.
al.'s model and show that our model is a stronger baseline for text to image
generation tasks.Comment: CVC 201
High-Quality Face Caricature via Style Translation
Caricature is an exaggerated form of artistic portraiture that accentuates
unique yet subtle characteristics of human faces. Recently, advancements in
deep end-to-end techniques have yielded encouraging outcomes in capturing both
style and elevated exaggerations in creating face caricatures. Most of these
approaches tend to produce cartoon-like results that could be more practical
for real-world applications. In this study, we proposed a high-quality,
unpaired face caricature method that is appropriate for use in the real world
and uses computer vision techniques and GAN models. We attain the exaggeration
of facial features and the stylization of appearance through a two-step
process: Face caricature generation and face caricature projection. The face
caricature generation step creates new caricature face datasets from real
images and trains a generative model using the real and newly created
caricature datasets. The Face caricature projection employs an encoder trained
with real and caricature faces with the pretrained generator to project real
and caricature faces. We perform an incremental facial exaggeration from the
real image to the caricature faces using the encoder and generator's latent
space. Our projection preserves the facial identity, attributes, and
expressions from the input image. Also, it accounts for facial occlusions, such
as reading glasses or sunglasses, to enhance the robustness of our model.
Furthermore, we conducted a comprehensive comparison of our approach with
various state-of-the-art face caricature methods, highlighting our process's
distinctiveness and exceptional realism.Comment: 14 pages, 21 figure
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