34,530 research outputs found
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
Interacting with Acoustic Simulation and Fabrication
Incorporating accurate physics-based simulation into interactive design tools
is challenging. However, adding the physics accurately becomes crucial to
several emerging technologies. For example, in virtual/augmented reality
(VR/AR) videos, the faithful reproduction of surrounding audios is required to
bring the immersion to the next level. Similarly, as personal fabrication is
made possible with accessible 3D printers, more intuitive tools that respect
the physical constraints can help artists to prototype designs. One main hurdle
is the sheer amount of computation complexity to accurately reproduce the
real-world phenomena through physics-based simulation. In my thesis research, I
develop interactive tools that implement efficient physics-based simulation
algorithms for automatic optimization and intuitive user interaction.Comment: ACM UIST 2017 Doctoral Symposiu
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
When Computer Vision Gazes at Cognition
Joint attention is a core, early-developing form of social interaction. It is
based on our ability to discriminate the third party objects that other people
are looking at. While it has been shown that people can accurately determine
whether another person is looking directly at them versus away, little is known
about human ability to discriminate a third person gaze directed towards
objects that are further away, especially in unconstraint cases where the
looker can move her head and eyes freely. In this paper we address this
question by jointly exploring human psychophysics and a cognitively motivated
computer vision model, which can detect the 3D direction of gaze from 2D face
images. The synthesis of behavioral study and computer vision yields several
interesting discoveries. (1) Human accuracy of discriminating targets
8{\deg}-10{\deg} of visual angle apart is around 40% in a free looking gaze
task; (2) The ability to interpret gaze of different lookers vary dramatically;
(3) This variance can be captured by the computational model; (4) Human
outperforms the current model significantly. These results collectively show
that the acuity of human joint attention is indeed highly impressive, given the
computational challenge of the natural looking task. Moreover, the gap between
human and model performance, as well as the variability of gaze interpretation
across different lookers, require further understanding of the underlying
mechanisms utilized by humans for this challenging task.Comment: Tao Gao and Daniel Harari contributed equally to this wor
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