36,155 research outputs found
In-Network View Synthesis for Interactive Multiview Video Systems
To enable Interactive multiview video systems with a minimum view-switching
delay, multiple camera views are sent to the users, which are used as reference
images to synthesize additional virtual views via depth-image-based rendering.
In practice, bandwidth constraints may however restrict the number of reference
views sent to clients per time unit, which may in turn limit the quality of the
synthesized viewpoints. We argue that the reference view selection should
ideally be performed close to the users, and we study the problem of in-network
reference view synthesis such that the navigation quality is maximized at the
clients. We consider a distributed cloud network architecture where data stored
in a main cloud is delivered to end users with the help of cloudlets, i.e.,
resource-rich proxies close to the users. In order to satisfy last-hop
bandwidth constraints from the cloudlet to the users, a cloudlet re-samples
viewpoints of the 3D scene into a discrete set of views (combination of
received camera views and virtual views synthesized) to be used as reference
for the synthesis of additional virtual views at the client. This in-network
synthesis leads to better viewpoint sampling given a bandwidth constraint
compared to simple selection of camera views, but it may however carry a
distortion penalty in the cloudlet-synthesized reference views. We therefore
cast a new reference view selection problem where the best subset of views is
defined as the one minimizing the distortion over a view navigation window
defined by the user under some transmission bandwidth constraints. We show that
the view selection problem is NP-hard, and propose an effective polynomial time
algorithm using dynamic programming to solve the optimization problem.
Simulation results finally confirm the performance gain offered by virtual view
synthesis in the network
Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter
The algorithm presented in this paper aims to segment the foreground objects in video (e.g., people) given time-varying, textured backgrounds. Examples of time-varying backgrounds include waves on water, clouds moving, trees waving in the wind, automobile traffic, moving crowds, escalators, etc. We have developed a novel foreground-background segmentation algorithm that explicitly accounts for the non-stationary nature and clutter-like appearance of many dynamic textures. The dynamic texture is modeled by an Autoregressive Moving Average Model (ARMA). A robust Kalman filter algorithm iteratively estimates the intrinsic appearance of the dynamic texture, as well as the regions of the foreground objects. Preliminary experiments with this method have demonstrated promising results
HeadOn: Real-time Reenactment of Human Portrait Videos
We propose HeadOn, the first real-time source-to-target reenactment approach
for complete human portrait videos that enables transfer of torso and head
motion, face expression, and eye gaze. Given a short RGB-D video of the target
actor, we automatically construct a personalized geometry proxy that embeds a
parametric head, eye, and kinematic torso model. A novel real-time reenactment
algorithm employs this proxy to photo-realistically map the captured motion
from the source actor to the target actor. On top of the coarse geometric
proxy, we propose a video-based rendering technique that composites the
modified target portrait video via view- and pose-dependent texturing, and
creates photo-realistic imagery of the target actor under novel torso and head
poses, facial expressions, and gaze directions. To this end, we propose a
robust tracking of the face and torso of the source actor. We extensively
evaluate our approach and show significant improvements in enabling much
greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at
Siggraph'1
Motion estimation and CABAC VLSI co-processors for real-time high-quality H.264/AVC video coding
Real-time and high-quality video coding is gaining a wide interest in the research and industrial community for different applications. H.264/AVC, a recent standard for high performance video coding, can be successfully exploited in several scenarios including digital video broadcasting, high-definition TV and DVD-based systems, which require to sustain up to tens of Mbits/s. To that purpose this paper proposes optimized architectures for H.264/AVC most critical tasks, Motion estimation and context adaptive binary arithmetic coding. Post synthesis results on sub-micron CMOS standard-cells technologies show that the proposed architectures can actually process in real-time 720 Ă 480 video sequences at 30 frames/s and grant more than 50 Mbits/s. The achieved circuit complexity and power consumption budgets are suitable for their integration in complex VLSI multimedia systems based either on AHB bus centric on-chip communication system or on novel Network-on-Chip (NoC) infrastructures for MPSoC (Multi-Processor System on Chip
Online Video Deblurring via Dynamic Temporal Blending Network
State-of-the-art video deblurring methods are capable of removing non-uniform
blur caused by unwanted camera shake and/or object motion in dynamic scenes.
However, most existing methods are based on batch processing and thus need
access to all recorded frames, rendering them computationally demanding and
time consuming and thus limiting their practical use. In contrast, we propose
an online (sequential) video deblurring method based on a spatio-temporal
recurrent network that allows for real-time performance. In particular, we
introduce a novel architecture which extends the receptive field while keeping
the overall size of the network small to enable fast execution. In doing so,
our network is able to remove even large blur caused by strong camera shake
and/or fast moving objects. Furthermore, we propose a novel network layer that
enforces temporal consistency between consecutive frames by dynamic temporal
blending which compares and adaptively (at test time) shares features obtained
at different time steps. We show the superiority of the proposed method in an
extensive experimental evaluation.Comment: 10 page
A robust loop-shaping approach to fast and accurate nanopositioning
Peer reviewedPreprin
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