3D hair sketching for real-time hair modeling and dynamic simulations

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.Thesis (Master's) -- Bilkent University, 2008.Includes bibliographical references leaves 48-51.Hair has been an active research area in computer graphics society for over a decade. Different approaches have been proposed for different aspects of hair research such as modeling, simulating, animating and rendering. In this thesis, we introduce a sketch-based tool making use of direct manipulation interfaces to create hair models and furthermore simulate the created hair models under physically based constraints in real-time. Throughout the thesis, the created tool will be analyzed with respect to different aspects of the problem such as hair modeling, hair simulation, hair sketching and hair rendering.Aras, RıfatM.S

3D hair design and key frame animation in real time

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2008.Thesis (Master's) -- Bilkent University, 2008.Includes bibliographical references leaves 54-57.Computer generated animations of humans, animals and all other kinds of objects have been studied extensively during the last two decades. The key for creating good animations has been to correctly imitate the behaviors of real objects and reflect these into computer generated images. With the rapid development of computer technology, creating realistic simulations has become possible, and the most striking components of these realistic animations happen to be the most dynamic (moving) parts; hair, in the case of human animations. With the development of high quality hair animations, the concern is not only creating physically correct animations, but also controlling these animations. An implementation of a key frame hair animation creation system, supported by a hair design tool, helping to model and animate hair easily, and provide these functionalities in real time is the aim of the proposed system. This work reviews several hair animation and sketching techniques, and proposes a system that provides a complete level of control (capable of controlling even the individual hair strands) of key frame animation and hair design in real time.Başarankut, BarkınM.S

3D Hair sketching for real-time dynamic & key frame animations

Physically based simulation of human hair is a well studied and well known problem. But the "pure" physically based representation of hair (and other animation elements) is not the only concern of the animators, who want to "control" the creation and animation phases of the content. This paper describes a sketch-based tool, with which a user can both create hair models with different styling parameters and produce animations of these created hair models using physically and key frame-based techniques. The model creation and animation production tasks are all performed with direct manipulation techniques in real-time. © 2008 Springer-Verlag

Chain Shape Matching for Simulating Complex Hairstyles

Animations of hair dynamics greatly enrich the visual attractiveness of human characters. Traditional simulation techniques handle hair as clumps or continuum for efficiency; however, the visual quality is limited because they cannot represent the fine-scale motion of individual hair strands. Although a recent mass-spring approach tackled the problem of simulating the dynamics of every strand of hair, it required a complicated setting of springs and suffered from high computational cost. In this paper, we base the animation of hair on such a fine-scale on Lattice Shape Matching (LSM), which has been successfully used for simulating deformable objects. Our method regards each strand of hair as a chain of particles, and computes geometrically derived forces for the chain based on shape matching. Each chain of particles is simulated as an individual strand of hair. Our method can easily handle complex hairstyles such as curly or afro styles in a numerically stable way. While our method is not physically based, our GPU-based simulator achieves visually plausible animations consisting of several tens of thousands of hair strands at interactive rates

Interactive Virtual Hair Salon

Abstract User interaction with animated hair is desirable for various applications but difficult because it requires real-time animation and rendering of hair. Hair modeling, in cluding styling, simulation, and rendering, is computationally challenging due to the enormous number of deformable hair strands on a human head, elevating the computational complexity of many essential steps, such as collision detection and self-shadowing for hair. Using simulation localization techniques, multi-resolution representations, and graphics hardware rendering acceleration, we have developed a physically-based virtual hair salon system that simulates and renders hair at accelerated rates, enabling users to interactively style virtual hair. With a 3D haptic interface, users can directly manipulate and position hair strands, as well as employ real-world styling applications (cutting, blow-drying, etc.) to create hairstyles more intuitively than previous techniques

Realistic Hair Simulation: Animation and Rendering

International audienceThe last five years have seen a profusion of innovative solutions to one of the most challenging tasks in character synthesis: hair simulation. This class covers both recent and novel research ideas in hair animation and rendering, and presents time tested industrial practices that resulted in spectacular imagery

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

GPU point list generation through histogram pyramids

Image Pyramids are frequently used in porting non-local algorithms to graphics hardware. A Histogram pyramid (short: HistoPyramid), a special version of image pyramid, sums up the number of active entries in a 2D image hierarchically. We show how a HistoPyramid can be utilized as an implicit indexing data structure, allowing us to convert a sparse matrix into a coordinate list of active cell entries (a point list) on graphics hardware . The algorithm reduces a highly sparse matrix with N elements to a list of its M active entries in O(N) + M (log N) steps, despite the restricted graphics hardware architecture. Applications are numerous, including feature detection, pixel classification and binning, conversion of 3D volumes to particle clouds and sparse matrix compression

Hairstyle modelling based on a single image.

Hair is an important feature to form character appearance in both film and video game industry. Hair grooming and combing for virtual characters was traditionally an exclusive task for professional designers because of its requirements for both technical manipulation and artistic inspiration. However, this manual process is time-consuming and further limits the flexibility of customised hairstyle modelling. In addition, it is hard to manipulate virtual hairstyle due to intrinsic hair shape. The fast development of related industrial applications demand an intuitive tool for efficiently creating realistic hairstyle for non-professional users. Recently, image-based hair modelling has been investigated for generating realistic hairstyle. This thesis demonstrates a framework Struct2Hair that robustly captures a hairstyle from a single portrait input. Specifically, the 2D hair strands are traced from the input with the help of image processing enhancement first. Then the 2D hair sketch of a hairstyle on a coarse level is extracted from generated 2D hair strands by clustering. To solve the inherently ill-posed single-view reconstruction problem, a critical hair shape database has been built by analysing an existing hairstyle model database. The critical hair shapes is a group of hair strands which possess similar shape appearance and close space location. Once the prior shape knowledge is prepared, the hair shape descriptor (HSD) is introduced to encode the structure of the target hairstyle. The HSD is constructed by retrieving and matching corresponding critical hair shape centres in the database. The full-head hairstyle is reconstructed by uniformly diffusing the hair strands on the scalp surface under the guidance of extracted HSD. The produced results are evaluated and compared with the state-of-the-art image based hair modelling methods. The findings of this thesis lead to some promising applications such as blending hairstyles to populate novel hair model, editing hairstyle (adding fringe hair, curling and cutting/extending hairstyle) and a case study of Bas-relief hair modelling on pre-processed hair images

Intuitive, Interactive Beard and Hair Synthesis with Generative Models

We present an interactive approach to synthesizing realistic variations in facial hair in images, ranging from subtle edits to existing hair to the addition of complex and challenging hair in images of clean-shaven subjects. To circumvent the tedious and computationally expensive tasks of modeling, rendering and compositing the 3D geometry of the target hairstyle using the traditional graphics pipeline, we employ a neural network pipeline that synthesizes realistic and detailed images of facial hair directly in the target image in under one second. The synthesis is controlled by simple and sparse guide strokes from the user defining the general structural and color properties of the target hairstyle. We qualitatively and quantitatively evaluate our chosen method compared to several alternative approaches. We show compelling interactive editing results with a prototype user interface that allows novice users to progressively refine the generated image to match their desired hairstyle, and demonstrate that our approach also allows for flexible and high-fidelity scalp hair synthesis.Comment: To be presented in the 2020 Conference on Computer Vision and Pattern Recognition (CVPR 2020, Oral Presentation). Supplementary video can be seen at: https://www.youtube.com/watch?v=v4qOtBATrv