Refining 3D Human Texture Estimation From a Single Image.

Estimating 3D human texture from a single image is essential in graphics and vision. It requires learning a mapping function from input images of humans with diverse poses into the parametric (uv) space and reasonably hallucinating invisible parts. To achieve a high-quality 3D human texture estimation, we propose a framework that adaptively samples the input by a deformable convolution where offsets are learned via a deep neural network.

Visualization of Large Non-Trivially Partitioned Unstructured Data with Native Distribution on High-Performance Computing Systems.

Interactively visualizing large finite element simulation data on High-Performance Computing (HPC) systems poses several difficulties. Some of these relate to unstructured data, which, even on a single node, is much more expensive to render compared to structured volume data. Worse yet, in the data parallel rendering context, such data with highly non-convex spatial domain boundaries will cause rays along its silhouette to enter and leave a given rank's domains at different distances.

Quick Clusters: A GPU-Parallel Partitioning for Efficient Path Tracing of Unstructured Volumetric Grids.

We propose a simple yet effective method for clustering finite elements to improve preprocessing times and rendering performance of unstructured volumetric grids without requiring auxiliary connectivity data. Rather than building bounding volume hierarchies (BVHs) over individual elements, we sort elements along with a Hilbert curve and aggregate neighboring elements together, improving BVH memory consumption by over an order of magnitude. Then to further reduce memory consumption, we cluster the mesh on the fly into sub-meshes with smaller indices using a series of efficient parallel mesh re-indexing operations.

Privacy-preserving and robust watermarking on sequential genome data using belief propagation and local differential privacy.

Motivation: Genome data is a subject of study for both biology and computer science since the start of the Human Genome Project in 1990. Since then, genome sequencing for medical and social purposes becomes more and more available and affordable. Genome data can be shared on public websites or with service providers (SPs).

Psychological Parameters for Crowd Simulation: From Audiences to Mobs.

In the social psychology literature, crowds are classified as audiences and mobs. Audiences are passive crowds, whereas mobs are active crowds with emotional, irrational and seemingly homogeneous behavior. In this study, we aim to create a system that enables the specification of different crowd types ranging from audiences to mobs.

MaterialVis: material visualization tool using direct volume and surface rendering techniques.

Visualization of the materials is an indispensable part of their structural analysis. We developed a visualization tool for amorphous as well as crystalline structures, called MaterialVis. Unlike the existing tools, MaterialVis represents material structures as a volume and a surface manifold, in addition to plain atomic coordinates.

How the Ocean personality model affects the perception of crowds.

This approach extends the HiDAC (High-Density Autonomous Crowds) system by providing each agent with a personality model based on the Ocean (openness, conscientiousness, extroversion, agreeableness, and neuroticism) personality model. Each personality trait has an associated nominal behavior. Specifying an agent's personality leads to an automation of low-level parameter tuning.

Rate-distortion efficient piecewise planar 3-D scene representation from 2-D images.

In any practical application of the 2-D-to-3-D conversion that involves storage and transmission, representation efficiency has an undisputable importance that is not reflected in the attention the topic received. In order to address this problem, a novel algorithm, which yields efficient 3-D representations in the rate distortion sense, is proposed. The algorithm utilizes two views of a scene to build a mesh-based representation incrementally, via adding new vertices, while minimizing a distortion measure.

Content-based retrieval of historical Ottoman documents stored as textual images.

There is an accelerating demand to access the visual content of documents stored in historical and cultural archives. Availability of electronic imaging tools and effective image processing techniques makes it feasible to process the multimedia data in large databases. In this paper, a framework for content-based retrieval of historical documents in the Ottoman Empire archives is presented.