233 research outputs found
Single Sketch Image based 3D Car Shape Reconstruction with Deep Learning and Lazy Learning
Efficient car shape design is a challenging problem in both the automotive industry and the computer animation/games industry. In this paper, we present a system to reconstruct the 3D car shape from a single 2D sketchimage. To learn the correlation between 2D sketches and 3D cars, we propose a Variational Autoencoder deepneural network that takes a 2D sketch and generates a set of multi-view depth and mask images, which forma more effective representation comparing to 3D meshes, and can be effectively fused to generate a 3D carshape. Since global models like deep learning have limited capacity to reconstruct fine-detail features, wepropose a local lazy learning approach that constructs a small subspace based on a few relevant car samples inthe database. Due to the small size of such a subspace, fine details can be represented effectively with a smallnumber of parameters. With a low-cost optimization process, a high-quality car shape with detailed featuresis created. Experimental results show that the system performs consistently to create highly realistic cars ofsubstantially different shape and topology
Interactive Sketching of Mannequin Poses
It can be easy and even fun to sketch humans in different poses. In contrast, creating those same poses on a 3D graphics 'mannequin' is comparatively tedious. Yet 3D body poses are necessary for various downstream applications. We seek to preserve the convenience of 2D sketching while giving users of different skill levels the flexibility to accurately and more quickly pose/refine a 3D mannequin. At the core of the interactive system, we propose a machine-learning model for inferring the 3D pose of a CG mannequin from sketches of humans drawn in a cylinder-person style. Training such a model is challenging because of artist variability, a lack of sketch training data with corresponding ground truth 3D poses, and the high dimensionality of human pose-space. Our unique approach to synthesizing vector graphics training data underpins our integrated ML-and-kinematics system. We validate the system by tightly coupling it with a user interface, and by performing a user study, in addition to quantitative comparisons
Fast character modeling with sketch-based PDE surfaces
© 2020, The Author(s). Virtual characters are 3D geometric models of characters. They have a lot of applications in multimedia. In this paper, we propose a new physics-based deformation method and efficient character modelling framework for creation of detailed 3D virtual character models. Our proposed physics-based deformation method uses PDE surfaces. Here PDE is the abbreviation of Partial Differential Equation, and PDE surfaces are defined as sculpting force-driven shape representations of interpolation surfaces. Interpolation surfaces are obtained by interpolating key cross-section profile curves and the sculpting force-driven shape representation uses an analytical solution to a vector-valued partial differential equation involving sculpting forces to quickly obtain deformed shapes. Our proposed character modelling framework consists of global modeling and local modeling. The global modeling is also called model building, which is a process of creating a whole character model quickly with sketch-guided and template-based modeling techniques. The local modeling produces local details efficiently to improve the realism of the created character model with four shape manipulation techniques. The sketch-guided global modeling generates a character model from three different levels of sketched profile curves called primary, secondary and key cross-section curves in three orthographic views. The template-based global modeling obtains a new character model by deforming a template model to match the three different levels of profile curves. Four shape manipulation techniques for local modeling are investigated and integrated into the new modelling framework. They include: partial differential equation-based shape manipulation, generalized elliptic curve-driven shape manipulation, sketch assisted shape manipulation, and template-based shape manipulation. These new local modeling techniques have both global and local shape control functions and are efficient in local shape manipulation. The final character models are represented with a collection of surfaces, which are modeled with two types of geometric entities: generalized elliptic curves (GECs) and partial differential equation-based surfaces. Our experiments indicate that the proposed modeling approach can build detailed and realistic character models easily and quickly
SKED: Sketch-guided Text-based 3D Editing
Text-to-image diffusion models are gradually introduced into computer
graphics, recently enabling the development of Text-to-3D pipelines in an open
domain. However, for interactive editing purposes, local manipulations of
content through a simplistic textual interface can be arduous. Incorporating
user guided sketches with Text-to-image pipelines offers users more intuitive
control. Still, as state-of-the-art Text-to-3D pipelines rely on optimizing
Neural Radiance Fields (NeRF) through gradients from arbitrary rendering views,
conditioning on sketches is not straightforward. In this paper, we present
SKED, a technique for editing 3D shapes represented by NeRFs. Our technique
utilizes as few as two guiding sketches from different views to alter an
existing neural field. The edited region respects the prompt semantics through
a pre-trained diffusion model. To ensure the generated output adheres to the
provided sketches, we propose novel loss functions to generate the desired
edits while preserving the density and radiance of the base instance. We
demonstrate the effectiveness of our proposed method through several
qualitative and quantitative experiments
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
DeepSketchHair: Deep Sketch-based 3D Hair Modeling
We present sketchhair, a deep learning based tool for interactive modeling of
3D hair from 2D sketches. Given a 3D bust model as reference, our sketching
system takes as input a user-drawn sketch (consisting of hair contour and a few
strokes indicating the hair growing direction within a hair region), and
automatically generates a 3D hair model, which matches the input sketch both
globally and locally. The key enablers of our system are two carefully designed
neural networks, namely, S2ONet, which converts an input sketch to a dense 2D
hair orientation field; and O2VNet, which maps the 2D orientation field to a 3D
vector field. Our system also supports hair editing with additional sketches in
new views. This is enabled by another deep neural network, V2VNet, which
updates the 3D vector field with respect to the new sketches. All the three
networks are trained with synthetic data generated from a 3D hairstyle
database. We demonstrate the effectiveness and expressiveness of our tool using
a variety of hairstyles and also compare our method with prior art
What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives
Intelligent Mesh Generation (IMG) represents a novel and promising field of
research, utilizing machine learning techniques to generate meshes. Despite its
relative infancy, IMG has significantly broadened the adaptability and
practicality of mesh generation techniques, delivering numerous breakthroughs
and unveiling potential future pathways. However, a noticeable void exists in
the contemporary literature concerning comprehensive surveys of IMG methods.
This paper endeavors to fill this gap by providing a systematic and thorough
survey of the current IMG landscape. With a focus on 113 preliminary IMG
methods, we undertake a meticulous analysis from various angles, encompassing
core algorithm techniques and their application scope, agent learning
objectives, data types, targeted challenges, as well as advantages and
limitations. We have curated and categorized the literature, proposing three
unique taxonomies based on key techniques, output mesh unit elements, and
relevant input data types. This paper also underscores several promising future
research directions and challenges in IMG. To augment reader accessibility, a
dedicated IMG project page is available at
\url{https://github.com/xzb030/IMG_Survey}
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