71,345 research outputs found
4D Human Body Capture from Egocentric Video via 3D Scene Grounding
We introduce a novel task of reconstructing a time series of second-person 3D
human body meshes from monocular egocentric videos. The unique viewpoint and
rapid embodied camera motion of egocentric videos raise additional technical
barriers for human body capture. To address those challenges, we propose a
simple yet effective optimization-based approach that leverages 2D observations
of the entire video sequence and human-scene interaction constraint to estimate
second-person human poses, shapes, and global motion that are grounded on the
3D environment captured from the egocentric view. We conduct detailed ablation
studies to validate our design choice. Moreover, we compare our method with the
previous state-of-the-art method on human motion capture from monocular video,
and show that our method estimates more accurate human-body poses and shapes
under the challenging egocentric setting. In addition, we demonstrate that our
approach produces more realistic human-scene interaction
Design and Optimization of Orthogonally Intersecting Planar Surfaces
We present a method for the design of 3D constructions from planar pieces that can be cut easily and cheaply with laser cutters and similar Computer Numerical Control (CNC) machines. By cutting tight slits in intersecting pieces, they can be slid into each other forming stable configurations without any gluing or additional connec- tors. These constructions enable quick prototyping and easy exploration of shapes, and are particularly useful for education. We propose a constraint-based optimization method and computational design framework to facilitate such structures
Efficient polar optimization of transport aircraft in transonic RANS flow using adjoint gradient based approach
A major design requirement for transport aircraft is efficient cruise flight in the transonic region. From the aerodynamic viewpoint, this is achieved by favorable lift-to-drag ratio of the aircraft, both at the main design point and at off-design conditions. We therefore present a method to efficiently perform a multi-point optimization of a representative wing-body configuration. Designs are evaluated with RANS CFD simulations, the wing is parametrized using 40 free-form deformation control points, and a gradient-based method is used to drive the optimization. The gradient of cost functions is computed with a discrete adjoint approach, in which flow and mesh adjoint equations are solved. Compared to single-point optimization, with multi-point optimization we obtain a design with slightly lower best lift-to-drag ratio, but which has improved lift-to-drag polar over the whole range of practical lift coefficients compared to the baseline design
Shape from Shading through Shape Evolution
In this paper, we address the shape-from-shading problem by training deep
networks with synthetic images. Unlike conventional approaches that combine
deep learning and synthetic imagery, we propose an approach that does not need
any external shape dataset to render synthetic images. Our approach consists of
two synergistic processes: the evolution of complex shapes from simple
primitives, and the training of a deep network for shape-from-shading. The
evolution generates better shapes guided by the network training, while the
training improves by using the evolved shapes. We show that our approach
achieves state-of-the-art performance on a shape-from-shading benchmark
Prototype system for supporting the incremental modelling of vague geometric configurations
In this paper the need for Intelligent Computer Aided Design (Int.CAD) to jointly support design and learning assistance is introduced. The paper focuses on presenting and exploring the possibility of realizing learning assistance in Int.CAD by introducing a new concept called Shared Learning. Shared Learning is proposed to empower CAD tools with more useful learning capabilities than that currently available and thereby provide a stronger interaction of learning between a designer and a computer. Controlled computational learning is proposed as a means whereby the Shared Learning concept can be realized. The viability of this new concept is explored by using a system called PERSPECT. PERSPECT is a preliminary numerical design tool aimed at supporting the effective utilization of numerical experiential knowledge in design. After a detailed discussion of PERSPECT's numerical design support, the paper presents the results of an evaluation that focuses on PERSPECT's implementation of controlled computational learning and ability to support a designer's need to learn. The paper then discusses PERSPECT's potential as a tool for supporting the Shared Learning concept by explaining how a designer and PERSPECT can jointly learn. There is still much work to be done before the full potential of Shared Learning can be realized. However, the authors do believe that the concept of Shared Learning may hold the key to truly empowering learning in Int.CAD
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Weakly supervised 3D Reconstruction with Adversarial Constraint
Supervised 3D reconstruction has witnessed a significant progress through the
use of deep neural networks. However, this increase in performance requires
large scale annotations of 2D/3D data. In this paper, we explore inexpensive 2D
supervision as an alternative for expensive 3D CAD annotation. Specifically, we
use foreground masks as weak supervision through a raytrace pooling layer that
enables perspective projection and backpropagation. Additionally, since the 3D
reconstruction from masks is an ill posed problem, we propose to constrain the
3D reconstruction to the manifold of unlabeled realistic 3D shapes that match
mask observations. We demonstrate that learning a log-barrier solution to this
constrained optimization problem resembles the GAN objective, enabling the use
of existing tools for training GANs. We evaluate and analyze the manifold
constrained reconstruction on various datasets for single and multi-view
reconstruction of both synthetic and real images
Nonparametric joint shape learning for customized shape modeling
We present a shape optimization approach to compute patient-specific models in customized prototyping applications. We design a coupled shape prior to model the transformation between a related pair of surfaces, using a nonparametric joint probability density estimation. The coupled shape prior forces with the help of application-specific data forces and smoothness forces drive a surface deformation
towards a desired output surface. We demonstrate the usefulness of the method for generating customized shape models in applications of hearing aid design and pre-operative to intra-operative anatomic surface estimation
Freeform User Interfaces for Graphical Computing
報告番号: 甲15222 ; 学位授与年月日: 2000-03-29 ; 学位の種別: 課程博士 ; 学位の種類: 博士(工学) ; 学位記番号: 博工第4717号 ; 研究科・専攻: 工学系研究科情報工学専
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