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View-dependent adaptive cloth simulation
This paper describes a method for view-dependent cloth simulation using dynamically adaptive mesh refinement and coarsening. Given a prescribed camera motion, the method adjusts the criteria controlling refinement to account for visibility and apparent size in the camera's view. Objectionable dynamic artifacts are avoided by anticipative refinement and smoothed coarsening. This approach preserves the appearance of detailed cloth throughout the animation while avoiding the wasted effort of simulating details that would not be discernible to the viewer. The computational savings realized by this method increase as scene complexity grows, producing a 2× speed-up for a single character and more than 4× for a small group
A Variational Stereo Method for the Three-Dimensional Reconstruction of Ocean Waves
We develop a novel remote sensing technique for the observation of waves on the ocean surface. Our method infers the 3-D waveform and radiance of oceanic sea states via a variational stereo imagery formulation. In this setting, the shape and radiance of the wave surface are given by minimizers of a composite energy functional that combines a photometric matching term along with regularization terms involving the smoothness of the unknowns. The desired ocean surface shape and radiance are the solution of a system of coupled partial differential equations derived from the optimality conditions of the energy functional. The proposed method is naturally extended to study the spatiotemporal dynamics of ocean waves and applied to three sets of stereo video data. Statistical and spectral analysis are carried out. Our results provide evidence that the observed omnidirectional wavenumber spectrum S(k) decays as k-2.5 is in agreement with Zakharov's theory (1999). Furthermore, the 3-D spectrum of the reconstructed wave surface is exploited to estimate wave dispersion and currents
RGB-D-based Action Recognition Datasets: A Survey
Human action recognition from RGB-D (Red, Green, Blue and Depth) data has
attracted increasing attention since the first work reported in 2010. Over this
period, many benchmark datasets have been created to facilitate the development
and evaluation of new algorithms. This raises the question of which dataset to
select and how to use it in providing a fair and objective comparative
evaluation against state-of-the-art methods. To address this issue, this paper
provides a comprehensive review of the most commonly used action recognition
related RGB-D video datasets, including 27 single-view datasets, 10 multi-view
datasets, and 7 multi-person datasets. The detailed information and analysis of
these datasets is a useful resource in guiding insightful selection of datasets
for future research. In addition, the issues with current algorithm evaluation
vis-\'{a}-vis limitations of the available datasets and evaluation protocols
are also highlighted; resulting in a number of recommendations for collection
of new datasets and use of evaluation protocols
Fast, invariant representation for human action in the visual system
Humans can effortlessly recognize others' actions in the presence of complex
transformations, such as changes in viewpoint. Several studies have located the
regions in the brain involved in invariant action recognition, however, the
underlying neural computations remain poorly understood. We use
magnetoencephalography (MEG) decoding and a dataset of well-controlled,
naturalistic videos of five actions (run, walk, jump, eat, drink) performed by
different actors at different viewpoints to study the computational steps used
to recognize actions across complex transformations. In particular, we ask when
the brain discounts changes in 3D viewpoint relative to when it initially
discriminates between actions. We measure the latency difference between
invariant and non-invariant action decoding when subjects view full videos as
well as form-depleted and motion-depleted stimuli. Our results show no
difference in decoding latency or temporal profile between invariant and
non-invariant action recognition in full videos. However, when either form or
motion information is removed from the stimulus set, we observe a decrease and
delay in invariant action decoding. Our results suggest that the brain
recognizes actions and builds invariance to complex transformations at the same
time, and that both form and motion information are crucial for fast, invariant
action recognition
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