188 research outputs found
A Fully Progressive Approach to Single-Image Super-Resolution
Recent deep learning approaches to single image super-resolution have
achieved impressive results in terms of traditional error measures and
perceptual quality. However, in each case it remains challenging to achieve
high quality results for large upsampling factors. To this end, we propose a
method (ProSR) that is progressive both in architecture and training: the
network upsamples an image in intermediate steps, while the learning process is
organized from easy to hard, as is done in curriculum learning. To obtain more
photorealistic results, we design a generative adversarial network (GAN), named
ProGanSR, that follows the same progressive multi-scale design principle. This
not only allows to scale well to high upsampling factors (e.g., 8x) but
constitutes a principled multi-scale approach that increases the reconstruction
quality for all upsampling factors simultaneously. In particular ProSR ranks
2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge
[34]. Compared to the top-ranking team, our model is marginally lower, but runs
5 times faster
Consistently Orienting Facets in Polygon Meshes by Minimizing the Dirichlet Energy of Generalized Winding Numbers
Jacobson et al. [JKSH13] hypothesized that the local coherency of the
generalized winding number function could be used to correctly determine
consistent facet orientations in polygon meshes. We report on an approach to
consistently orienting facets in polygon meshes by minimizing the Dirichlet
energy of generalized winding numbers. While the energy can be concisely
formulated and efficiently computed, we found that this approach is
fundamentally flawed and is unfortunately not applicable for most handmade
meshes shared on popular mesh repositories such as Google 3D Warehouse.Comment: 6 pages, 4 figure
GarmentCode: Programming Parametric Sewing Patterns
Garment modeling is an essential task of the global apparel industry and a
core part of digital human modeling. Realistic representation of garments with
valid sewing patterns is key to their accurate digital simulation and eventual
fabrication. However, little-to-no computational tools provide support for
bridging the gap between high-level construction goals and low-level editing of
pattern geometry, e.g., combining or switching garment elements, semantic
editing, or design exploration that maintains the validity of a sewing pattern.
We suggest the first DSL for garment modeling -- GarmentCode -- that applies
principles of object-oriented programming to garment construction and allows
designing sewing patterns in a hierarchical, component-oriented manner. The
programming-based paradigm naturally provides unique advantages of component
abstraction, algorithmic manipulation, and free-form design parametrization. We
additionally support the construction process by automating typical low-level
tasks like placing a dart at a desired location. In our prototype garment
configurator, users can manipulate meaningful design parameters and body
measurements, while the construction of pattern geometry is handled by garment
programs implemented with GarmentCode. Our configurator enables the free
exploration of rich design spaces and the creation of garments using
interchangeable, parameterized components. We showcase our approach by
producing a variety of garment designs and retargeting them to different body
shapes using our configurator.Comment: Supplementary video: https://youtu.be/16Yyr2G9_6E
Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields
We present implicit displacement fields, a novel representation for detailed
3D geometry. Inspired by a classic surface deformation technique, displacement
mapping, our method represents a complex surface as a smooth base surface plus
a displacement along the base's normal directions, resulting in a
frequency-based shape decomposition, where the high frequency signal is
constrained geometrically by the low frequency signal. Importantly, this
disentanglement is unsupervised thanks to a tailored architectural design that
has an innate frequency hierarchy by construction. We explore implicit
displacement field surface reconstruction and detail transfer and demonstrate
superior representational power, training stability and generalizability.Comment: includes supplementary; ver2 corrected typos in eq(1
Patch-based Progressive 3D Point Set Upsampling
We present a detail-driven deep neural network for point set upsampling. A
high-resolution point set is essential for point-based rendering and surface
reconstruction. Inspired by the recent success of neural image super-resolution
techniques, we progressively train a cascade of patch-based upsampling networks
on different levels of detail end-to-end. We propose a series of architectural
design contributions that lead to a substantial performance boost. The effect
of each technical contribution is demonstrated in an ablation study.
Qualitative and quantitative experiments show that our method significantly
outperforms the state-of-the-art learning-based and optimazation-based
approaches, both in terms of handling low-resolution inputs and revealing
high-fidelity details.Comment: accepted to cvpr2019, code available at https://github.com/yifita/P3
Robust Inside-Outside Segmentation Using Generalized Winding Numbers
Solid shapes in computer graphics are often represented with boundary descriptions, e.g. triangle meshes, but animation, physicallybased simulation, and geometry processing are more realistic and accurate when explicit volume representations are available. Tetrahedral meshes which exactly contain (interpolate) the input boundary description are desirable but difficult to construct for a large class of input meshes. Character meshes and CAD models are often composed of many connected components with numerous selfintersections, non-manifold pieces, and open boundaries, precluding existing meshing algorithms. We propose an automatic algorithm handling all of these issues, resulting in a compact discretization of the input’s inner volume. We only require reasonably consistent orientation of the input triangle mesh. By generalizing the winding number for arbitrary triangle meshes, we define a function that is a perfect segmentation for watertight input and is well-behaved otherwise. This function guides a graphcut segmentation of a constrained Delaunay tessellation (CDT), providing a minimal description that meets the boundary exactly and may be fed as input to existing tools to achieve element quality. We highlight our robustness on a number of examples and show applications of solving PDEs, volumetric texturing and elastic simulation
Computational Smocking through Fabric-Thread Interaction
We formalize Italian smocking, an intricate embroidery technique that gathers
flat fabric into pleats along meandering lines of stitches, resulting in pleats
that fold and gather where the stitching veers. In contrast to English
smocking, characterized by colorful stitches decorating uniformly shaped
pleats, and Canadian smocking, which uses localized knots to form voluminous
pleats, Italian smocking permits the fabric to move freely along the stitched
threads following curved paths, resulting in complex and unpredictable pleats
with highly diverse, irregular structures, achieved simply by pulling on the
threads. We introduce a novel method for digital previewing of Italian smocking
results, given the thread stitching path as input. Our method uses a
coarse-grained mass-spring system to simulate the interaction between the
threads and the fabric. This configuration guides the fine-level fabric
deformation through an adaptation of the state-of-the-art simulator, C-IPC. Our
method models the general problem of fabric-thread interaction and can be
readily adapted to preview Canadian smocking as well. We compare our results to
baseline approaches and physical fabrications to demonstrate the accuracy of
our method
Digital 3D Smocking Design
We develop an optimization-based method to model smocking, a surface
embroidery technique that provides decorative geometric texturing while
maintaining stretch properties of the fabric. During smocking, multiple pairs
of points on the fabric are stitched together, creating non-manifold geometric
features and visually pleasing textures. Designing smocking patterns is
challenging, because the outcome of stitching is unpredictable: the final
texture is often revealed only when the whole smocking process is completed,
necessitating painstaking physical fabrication and time consuming
trial-and-error experimentation. This motivates us to seek a digital smocking
design method. Straightforward attempts to compute smocked fabric geometry
using surface deformation or cloth simulation methods fail to produce realistic
results, likely due to the intricate structure of the designs, the large number
of contacts and high-curvature folds. We instead formulate smocking as a graph
embedding and shape deformation problem. We extract a coarse graph representing
the fabric and the stitching constraints, and then derive the graph structure
of the smocked result. We solve for the 3D embedding of this graph, which in
turn reliably guides the deformation of the high-resolution fabric mesh. Our
optimization based method is simple, efficient, and flexible, which allows us
to build an interactive system for smocking pattern exploration. To demonstrate
the accuracy of our method, we compare our results to real fabrications on a
large set of smocking patternsComment: 17 pages, 35 figure
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