15,497 research outputs found
Multi-Content GAN for Few-Shot Font Style Transfer
In this work, we focus on the challenge of taking partial observations of
highly-stylized text and generalizing the observations to generate unobserved
glyphs in the ornamented typeface. To generate a set of multi-content images
following a consistent style from very few examples, we propose an end-to-end
stacked conditional GAN model considering content along channels and style
along network layers. Our proposed network transfers the style of given glyphs
to the contents of unseen ones, capturing highly stylized fonts found in the
real-world such as those on movie posters or infographics. We seek to transfer
both the typographic stylization (ex. serifs and ears) as well as the textual
stylization (ex. color gradients and effects.) We base our experiments on our
collected data set including 10,000 fonts with different styles and demonstrate
effective generalization from a very small number of observed glyphs
Depth Assisted Full Resolution Network for Single Image-based View Synthesis
Researches in novel viewpoint synthesis majorly focus on interpolation from
multi-view input images. In this paper, we focus on a more challenging and
ill-posed problem that is to synthesize novel viewpoints from one single input
image. To achieve this goal, we propose a novel deep learning-based technique.
We design a full resolution network that extracts local image features with the
same resolution of the input, which contributes to derive high resolution and
prevent blurry artifacts in the final synthesized images. We also involve a
pre-trained depth estimation network into our system, and thus 3D information
is able to be utilized to infer the flow field between the input and the target
image. Since the depth network is trained by depth order information between
arbitrary pairs of points in the scene, global image features are also involved
into our system. Finally, a synthesis layer is used to not only warp the
observed pixels to the desired positions but also hallucinate the missing
pixels with recorded pixels. Experiments show that our technique performs well
on images of various scenes, and outperforms the state-of-the-art techniques
SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes
The objective of this paper is 3D shape understanding from single and
multiple images. To this end, we introduce a new deep-learning architecture and
loss function, SilNet, that can handle multiple views in an order-agnostic
manner. The architecture is fully convolutional, and for training we use a
proxy task of silhouette prediction, rather than directly learning a mapping
from 2D images to 3D shape as has been the target in most recent work.
We demonstrate that with the SilNet architecture there is generalisation over
the number of views -- for example, SilNet trained on 2 views can be used with
3 or 4 views at test-time; and performance improves with more views.
We introduce two new synthetics datasets: a blobby object dataset useful for
pre-training, and a challenging and realistic sculpture dataset; and
demonstrate on these datasets that SilNet has indeed learnt 3D shape. Finally,
we show that SilNet exceeds the state of the art on the ShapeNet benchmark
dataset, and use SilNet to generate novel views of the sculpture dataset.Comment: BMVC 2017; Best Poste
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