509 research outputs found
DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
Face modeling has been paid much attention in the field of visual computing.
There exist many scenarios, including cartoon characters, avatars for social
media, 3D face caricatures as well as face-related art and design, where
low-cost interactive face modeling is a popular approach especially among
amateur users. In this paper, we propose a deep learning based sketching system
for 3D face and caricature modeling. This system has a labor-efficient
sketching interface, that allows the user to draw freehand imprecise yet
expressive 2D lines representing the contours of facial features. A novel CNN
based deep regression network is designed for inferring 3D face models from 2D
sketches. Our network fuses both CNN and shape based features of the input
sketch, and has two independent branches of fully connected layers generating
independent subsets of coefficients for a bilinear face representation. Our
system also supports gesture based interactions for users to further manipulate
initial face models. Both user studies and numerical results indicate that our
sketching system can help users create face models quickly and effectively. A
significantly expanded face database with diverse identities, expressions and
levels of exaggeration is constructed to promote further research and
evaluation of face modeling techniques.Comment: 12 pages, 16 figures, to appear in SIGGRAPH 201
CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution
Medical image arbitrary-scale super-resolution (MIASSR) has recently gained
widespread attention, aiming to super sample medical volumes at arbitrary
scales via a single model. However, existing MIASSR methods face two major
limitations: (i) reliance on high-resolution (HR) volumes and (ii) limited
generalization ability, which restricts their application in various scenarios.
To overcome these limitations, we propose Cube-based Neural Radiance Field
(CuNeRF), a zero-shot MIASSR framework that can yield medical images at
arbitrary scales and viewpoints in a continuous domain. Unlike existing MIASSR
methods that fit the mapping between low-resolution (LR) and HR volumes, CuNeRF
focuses on building a coordinate-intensity continuous representation from LR
volumes without the need for HR references. This is achieved by the proposed
differentiable modules: including cube-based sampling, isotropic volume
rendering, and cube-based hierarchical rendering. Through extensive experiments
on magnetic resource imaging (MRI) and computed tomography (CT) modalities, we
demonstrate that CuNeRF outperforms state-of-the-art MIASSR methods. CuNeRF
yields better visual verisimilitude and reduces aliasing artifacts at various
upsampling factors. Moreover, our CuNeRF does not need any LR-HR training
pairs, which is more flexible and easier to be used than others. Our code will
be publicly available soon
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