1,382 research outputs found
Multi-View Sketch-based FreeForm Modeling
International audienceFor the generation of freeform 3D models, one of the most intuitive solution is to use sketch-based modeling environments. Unfortunately, since the user interface relies upon the analyze of sketches in order to determine which action is requested by the user, the possible amount of different operations can be limited. In this paper, we present a 3D sketching system based on multiple views. Each view is specialized on a component of the modeling process (like the skeleton, the profile, etc.), and is based on specific sketching interactions. With this approach, an user could improve its understanding of the modeling process and per- form a larger range of modeling operations. Key words: Sketch-based 3D Modelin
Neural networks based recognition of 3D freeform surface from 2D sketch
In this paper, the Back Propagation (BP) network and Radial Basis Function (RBF) neural network are employed to recognize and reconstruct 3D freeform surface from 2D freehand sketch. Some tests and comparison experiments have been made to evaluate the performance for the reconstruction of freeform surfaces of both networks using simulation data. The experimental results show that both BP and RBF based freeform surface reconstruction methods are feasible; and the RBF network performed better. The RBF average point error between the reconstructed 3D surface data and the desired 3D surface data is less than 0.05 over all our 75 test sample data
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
Sketching-out virtual humans: From 2d storyboarding to immediate 3d character animation
Virtual beings are playing a remarkable role in today’s public entertainment, while ordinary users are still treated as audiences due to the lack of appropriate expertise, equipment, and computer skills. In this paper, we present a fast and intuitive storyboarding interface, which enables users to sketch-out 3D virtual humans, 2D/3D animations, and character intercommunication. We devised an intuitive “stick figurefleshing-outskin mapping” graphical animation pipeline, which realises the whole process of key framing, 3D pose reconstruction, virtual human modelling, motion path/timing control, and the final animation synthesis by almost pure 2D sketching. A “creative model-based method” is developed, which emulates a human perception process, to generate the 3D human bodies of variational sizes, shapes, and fat distributions. Meanwhile, our current system also supports the sketch-based crowd animation and the storyboarding of the 3D multiple character intercommunication. This system has been formally tested by various users on Tablet PC. After minimal training, even a beginner can create vivid virtual humans and animate them within minutes
Progressive surface modeling scheme from unorganised curves
This paper presents a novel surface modelling scheme to construct a freeform surface
progressively from unorganised curves representing the boundary and interior characteristic curves.
The approach can construct a base surface model from four ordinary or composite boundary curves
and support incremental surface updating from interior characteristic curves, some of which may not
be on the final surface. The base surface is first constructed as a regular Coons surface and upon receiving an interior curve sketch, it is then updated. With this progressive modelling scheme, a final
surface with multiple sub-surfaces can be obtained from a set of unorganised curves and transferred
to commercial surface modelling software for detailed modification. The approach has been tested
with examples based on 3D motion sketches; it is capable of dealing with unorganised design curves
for surface modelling in conceptual design. Its limitations have been discussed
DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
Face modeling has been paid much attention in the field of visual computing.
There exist many scenarios, including cartoon characters, avatars for social
media, 3D face caricatures as well as face-related art and design, where
low-cost interactive face modeling is a popular approach especially among
amateur users. In this paper, we propose a deep learning based sketching system
for 3D face and caricature modeling. This system has a labor-efficient
sketching interface, that allows the user to draw freehand imprecise yet
expressive 2D lines representing the contours of facial features. A novel CNN
based deep regression network is designed for inferring 3D face models from 2D
sketches. Our network fuses both CNN and shape based features of the input
sketch, and has two independent branches of fully connected layers generating
independent subsets of coefficients for a bilinear face representation. Our
system also supports gesture based interactions for users to further manipulate
initial face models. Both user studies and numerical results indicate that our
sketching system can help users create face models quickly and effectively. A
significantly expanded face database with diverse identities, expressions and
levels of exaggeration is constructed to promote further research and
evaluation of face modeling techniques.Comment: 12 pages, 16 figures, to appear in SIGGRAPH 201
Sketching-out virtual humans: A smart interface for human modelling and animation
In this paper, we present a fast and intuitive interface for sketching out
3D virtual humans and animation. The user draws stick figure key frames first and
chooses one for “fleshing-out” with freehand body contours. The system
automatically constructs a plausible 3D skin surface from the rendered figure, and
maps it onto the posed stick figures to produce the 3D character animation. A
“creative model-based method” is developed, which performs a human perception
process to generate 3D human bodies of various body sizes, shapes and fat
distributions. In this approach, an anatomical 3D generic model has been created with
three distinct layers: skeleton, fat tissue, and skin. It can be transformed sequentially
through rigid morphing, fatness morphing, and surface fitting to match the original
2D sketch. An auto-beautification function is also offered to regularise the 3D
asymmetrical bodies from users’ imperfect figure sketches. Our current system
delivers character animation in various forms, including articulated figure animation,
3D mesh model animation, 2D contour figure animation, and even 2D NPR animation
with personalised drawing styles. The system has been formally tested by various
users on Tablet PC. After minimal training, even a beginner can create vivid virtual
humans and animate them within minutes
3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks
We propose a method for reconstructing 3D shapes from 2D sketches in the form
of line drawings. Our method takes as input a single sketch, or multiple
sketches, and outputs a dense point cloud representing a 3D reconstruction of
the input sketch(es). The point cloud is then converted into a polygon mesh. At
the heart of our method lies a deep, encoder-decoder network. The encoder
converts the sketch into a compact representation encoding shape information.
The decoder converts this representation into depth and normal maps capturing
the underlying surface from several output viewpoints. The multi-view maps are
then consolidated into a 3D point cloud by solving an optimization problem that
fuses depth and normals across all viewpoints. Based on our experiments,
compared to other methods, such as volumetric networks, our architecture offers
several advantages, including more faithful reconstruction, higher output
surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral
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