2,714 research outputs found
Human Motion Capture Data Tailored Transform Coding
Human motion capture (mocap) is a widely used technique for digitalizing
human movements. With growing usage, compressing mocap data has received
increasing attention, since compact data size enables efficient storage and
transmission. Our analysis shows that mocap data have some unique
characteristics that distinguish themselves from images and videos. Therefore,
directly borrowing image or video compression techniques, such as discrete
cosine transform, does not work well. In this paper, we propose a novel
mocap-tailored transform coding algorithm that takes advantage of these
features. Our algorithm segments the input mocap sequences into clips, which
are represented in 2D matrices. Then it computes a set of data-dependent
orthogonal bases to transform the matrices to frequency domain, in which the
transform coefficients have significantly less dependency. Finally, the
compression is obtained by entropy coding of the quantized coefficients and the
bases. Our method has low computational cost and can be easily extended to
compress mocap databases. It also requires neither training nor complicated
parameter setting. Experimental results demonstrate that the proposed scheme
significantly outperforms state-of-the-art algorithms in terms of compression
performance and speed
Transport-Based Neural Style Transfer for Smoke Simulations
Artistically controlling fluids has always been a challenging task.
Optimization techniques rely on approximating simulation states towards target
velocity or density field configurations, which are often handcrafted by
artists to indirectly control smoke dynamics. Patch synthesis techniques
transfer image textures or simulation features to a target flow field. However,
these are either limited to adding structural patterns or augmenting coarse
flows with turbulent structures, and hence cannot capture the full spectrum of
different styles and semantically complex structures. In this paper, we propose
the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric
smoke data. Our method is able to transfer features from natural images to
smoke simulations, enabling general content-aware manipulations ranging from
simple patterns to intricate motifs. The proposed algorithm is physically
inspired, since it computes the density transport from a source input smoke to
a desired target configuration. Our transport-based approach allows direct
control over the divergence of the stylization velocity field by optimizing
incompressible and irrotational potentials that transport smoke towards
stylization. Temporal consistency is ensured by transporting and aligning
subsequent stylized velocities, and 3D reconstructions are computed by
seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional
materials: http://www.byungsoo.me/project/neural-flow-styl
A Survey of Ocean Simulation and Rendering Techniques in Computer Graphics
This paper presents a survey of ocean simulation and rendering methods in
computer graphics. To model and animate the ocean's surface, these methods
mainly rely on two main approaches: on the one hand, those which approximate
ocean dynamics with parametric, spectral or hybrid models and use empirical
laws from oceanographic research. We will see that this type of methods
essentially allows the simulation of ocean scenes in the deep water domain,
without breaking waves. On the other hand, physically-based methods use
Navier-Stokes Equations (NSE) to represent breaking waves and more generally
ocean surface near the shore. We also describe ocean rendering methods in
computer graphics, with a special interest in the simulation of phenomena such
as foam and spray, and light's interaction with the ocean surface
Real Time Animation of Virtual Humans: A Trade-off Between Naturalness and Control
Virtual humans are employed in many interactive applications using 3D virtual environments, including (serious) games. The motion of such virtual humans should look realistic (or ‘natural’) and allow interaction with the surroundings and other (virtual) humans. Current animation techniques differ in the trade-off they offer between motion naturalness and the control that can be exerted over the motion. We show mechanisms to parametrize, combine (on different body parts) and concatenate motions generated by different animation techniques. We discuss several aspects of motion naturalness and show how it can be evaluated. We conclude by showing the promise of combinations of different animation paradigms to enhance both naturalness and control
The minimum energy expenditure shortest path method
This article discusses the addition of an energy parameter to the shortest path execution process; namely, the energy expenditure by a character during execution of the path. Given a simple environment in which a character has the ability to perform actions related to locomotion, such as walking and stair stepping, current techniques execute the shortest path based on the length of the extracted root trajectory. However, actual humans acting in constrained environments do not plan only according to shortest path criterion, they conceptually measure the path that minimizes the amount of energy expenditure. On this basis, it seems that virtual characters should also execute their paths according to the minimization of actual energy expenditure as well. In this article, a simple method that uses a formula for computing vanadium dioxide () levels, which is a proxy for the energy expenditure by humans during various activities, is presented. The presented solution could be beneficial in any situation requiring a sophisticated perspective of the path-execution process. Moreover, it can be implemented in almost every path-planning method that has the ability to measure stepping actions or other actions of a virtual character
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