3,883 research outputs found
VolTeMorph: Realtime, Controllable and Generalisable Animation of Volumetric Representations
The recent increase in popularity of volumetric representations for scene
reconstruction and novel view synthesis has put renewed focus on animating
volumetric content at high visual quality and in real-time. While implicit
deformation methods based on learned functions can produce impressive results,
they are `black boxes' to artists and content creators, they require large
amounts of training data to generalise meaningfully, and they do not produce
realistic extrapolations outside the training data. In this work we solve these
issues by introducing a volume deformation method which is real-time, easy to
edit with off-the-shelf software and can extrapolate convincingly. To
demonstrate the versatility of our method, we apply it in two scenarios:
physics-based object deformation and telepresence where avatars are controlled
using blendshapes. We also perform thorough experiments showing that our method
compares favourably to both volumetric approaches combined with implicit
deformation and methods based on mesh deformation.Comment: 18 pages, 21 figure
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Virtual viewpoint three-dimensional panorama
Conventional panoramic images are known to provide for an enhanced field of view in which the scene
always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual
viewpoint creation to generate different panoramic images of the same scene with three-dimensional
component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of
panoramic images
Thermodynamic analysis of nutating disc engine topping cycles for aero-engine applications
Within the next thirty years the evolutionary approach to aero engine development will struggle to keep abreast with increasingly stringent environmental targets. Therefore radical approaches to aero-engine development in terms of energy savings need to be considered. One particular concept involves the inclusion of a pressure-rise combustion system, within the architecture of an aero-engine, to provide additional shaft power. The nutating disc engine concept is a strong contender due to its power density. The feasibility of the nutating disc engine has been previously investigated for unmanned vehicle applications. However, this paper investigates the performance benefits of incorporating a nutating disc core in a larger geared open rotor engine for a potential entry in to service in 2050. In addition, a methodology is presented to estimate the size and weight of the nutating disc core. This methodology is pivotal in determining the overall performance of the novel aero-engine cycle. The outcome of this study predicts a potential 9.4% fuel burn benefit, over a state of the art geared open rotor in the year 2050. In addition, the sensitivity of the nutating disc design variables highlights the possible fuel burn benefits compared against a comparable year-2000 aircraft mission
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End-to-end 3D video communication over heterogeneous networks
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Three-dimensional technology, more commonly referred to as 3D technology, has revolutionised many fields including entertainment, medicine, and communications to name a few. In addition to 3D films, games, and sports channels, 3D perception has made tele-medicine a reality. By the year 2015, 30% of the all HD panels at home will be 3D enabled, predicted by consumer electronics manufacturers. Stereoscopic cameras, a comparatively mature technology compared to other 3D systems, are now being used by ordinary citizens to produce 3D content and share at a click of a button just like they do with the 2D counterparts via sites like YouTube. But technical challenges still exist, including with autostereoscopic multiview displays. 3D content requires many complex considerations--including how to represent it, and deciphering what is the best compression format--when considering transmission or storage, because of its increased amount of data. Any decision must be taken in the light of the available bandwidth or storage capacity, quality and user expectations. Free viewpoint navigation also remains partly unsolved. The most pressing issue getting in the way of widespread uptake of consumer 3D systems is the ability to deliver 3D content to heterogeneous consumer displays over the heterogeneous networks. Optimising 3D video communication solutions must consider the entire pipeline, starting with optimisation at the video source to the end display and transmission optimisation. Multi-view offers the most compelling solution for 3D videos with motion parallax and freedom from wearing headgear for 3D video perception. Optimising multi-view video for delivery and display could increase the demand for true 3D in the consumer market. This thesis focuses on an end-to-end quality optimisation in 3D video communication/transmission, offering solutions for optimisation at the compression, transmission, and decoder levels.Brunel University - Isambard Research Scholarshi
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation
Generative models for 3D geometric data arise in many important applications
in 3D computer vision and graphics. In this paper, we focus on 3D deformable
shapes that share a common topological structure, such as human faces and
bodies. Morphable Models and their variants, despite their linear formulation,
have been widely used for shape representation, while most of the recently
proposed nonlinear approaches resort to intermediate representations, such as
3D voxel grids or 2D views. In this work, we introduce a novel graph
convolutional operator, acting directly on the 3D mesh, that explicitly models
the inductive bias of the fixed underlying graph. This is achieved by enforcing
consistent local orderings of the vertices of the graph, through the spiral
operator, thus breaking the permutation invariance property that is adopted by
all the prior work on Graph Neural Networks. Our operator comes by construction
with desirable properties (anisotropic, topology-aware, lightweight,
easy-to-optimise), and by using it as a building block for traditional deep
generative architectures, we demonstrate state-of-the-art results on a variety
of 3D shape datasets compared to the linear Morphable Model and other graph
convolutional operators.Comment: to appear at ICCV 201
Representation and coding of 3D video data
Livrable D4.1 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D4.1 du projet
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