Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs). Traditional image-to-image translation networks can generate recognizable results, but finer textures are usually lost and blur artifacts commonly exist. In this paper, we propose a two-phase generative model for high-quality hair image synthesis. The two-phase pipeline first generates a coarse image by an existing image translation model, then applies a re-generating network with self-enhancing capability to the coarse image. The self-enhancing capability is achieved by a proposed structure extraction layer, which extracts the texture and orientation map from a hair image. Extensive experiments on two tasks, Sketch2Hair and Hair Super-Resolution, demonstrate that our approach is able to synthesize plausible hair image with finer details, and outperforms the state-of-the-art.
Generating virtual hairstyles is a long-standing research topic in computer graphics due to the important role it plays in representing human characters in games and movies. Most previous works focus on producing 3D hairs, according to user interactions @cite_16 @cite_43 @cite_15 or real-captured images @cite_20 @cite_33 @cite_35 @cite_12 . Given images, thanks to these modeling techniques, the hair can be recovered strand by strand which enables intelligent hair editing @cite_34 @cite_44 or interpolation @cite_11 by performing manipulation in 3D space and then being re-rendered to 2D domain. Although these methods are able to result in realistic appearances, high computational cost are incurred due to the involvement of 3D matters. To avoid high computational cost in hair rendering, @cite_38 proposes a deep learning based hair synthesis method, which can generate high-quality results from an edge activation map. However, to obtain the activation map, an input CG hair model is still required for the initial rendering. In comparison, our method involves no 3D rendering module, and relies on a 2D image with sparse information only. With such a limited input, we still synthesize photo-realistic results thanks to the self-enhancing module as proposed.
@cite_35 @cite_38 @cite_33 @cite_34 @cite_44 @cite_43 @cite_15 @cite_16 @cite_12 @cite_20 @cite_11
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We introduce AutoHair, the first fully automatic method for 3D hair modeling from a single portrait image, with no user interaction or parameter tuning. Our method efficiently generates complete and high-quality hair geometries, which are comparable to those generated by the state-of-the-art methods, where user interaction is required. The core components of our method are: a novel hierarchical deep neural network for automatic hair segmentation and hair growth direction estimation, trained over an annotated hair image database; and an efficient and automatic data-driven hair matching and modeling algorithm, based on a large set of 3D hair exemplars. We demonstrate the efficacy and robustness of our method on Internet photos, resulting in a database of around 50K 3D hair models and a corresponding hairstyle space that covers a wide variety of real-world hairstyles. We also show novel applications enabled by our method, including 3D hairstyle space navigation and hair-aware image retrieval. We present an adversarial network for rendering photorealistic hair as an alternative to conventional computer graphics pipelines. Our deep learning approach does not require low-level parameter tuning nor ad-hoc asset design. Our method simply takes a strand-based 3D hair model as input and provides intuitive user-control for color and lighting through reference images. To handle the diversity of hairstyles and its appearance complexity, we disentangle hair structure, color, and illumination properties using a sequential GAN architecture and a semi-supervised training approach. We also introduce an intermediate edge activation map to orientation field conversion step to ensure a successful CG-to-photoreal transition, while preserving the hair structures of the original input data. As we only require a feed-forward pass through the network, our rendering performs in real-time. We demonstrate the synthesis of photorealistic hair images on a wide range of intricate hairstyles and compare our technique with state-of-the-art hair rendering methods. This paper presents a single-view hair modeling technique for generating visually and physically plausible 3D hair models with modest user interaction. By solving an unambiguous 3D vector field explicitly from the image and adopting an iterative hair generation algorithm, we can create hair models that not only visually match the original input very well but also possess physical plausibility (e.g., having strand roots fixed on the scalp and preserving the length and continuity of real strands in the image as much as possible). The latter property enables us to manipulate hair in many new ways that were previously very difficult with a single image, such as dynamic simulation or interactive hair shape editing. We further extend the modeling approach to handle simple video input, and generate dynamic 3D hair models. This allows users to manipulate hair in a video or transfer styles from images to videos. We are pleased to present the proceedings of the Fourth Eurographics Workshop on Sketch-Based Interfaces and Modeling (SBIM '07). This annual workshop explores techniques and technologies needed to enable sketchbased interfaces. This year's workshop took place in Riverside, California, on the University of California, Riverside campus from August 2-4, coinciding with the 2007 ACM SIGGRAPH conference in San Diego, California. The program included paper presentations, invited talks, a hands-on demo session, and a break-out brainstorming session. This year's 18 paper presentations addressed novel work in a wide-range of exciting areas in sketchbased interfaces and modeling including sketch recognition, ink processing, user interface design, and 3D modeling. In Thursday's keynote presentation, James Landay, Associate Professor at the University of Washington and Strategic Advisor at Intel Research, proposed activity-based pen computing as a new model for the design of pen-based interfaces. In Friday's invited talk, Andrew Forsberg, a Research Scientist at Brown University, discussed ways to strengthen the pen computing research community. In the hands-on demo session, participants had the opportunity to try out eight novel pen-based interfaces. On Saturday, members of Microsoft Corp. and the Brown University Center for Pen Based Computing led a hands-on technical development tutorial for the Tablet PC. Participants at the tutorial created a simple application to collect and manipulate ink, and learned the latest details of working with new APIs, such as the Real-Time Stylus and the Handwriting Recognition APIs. The workshop included presentations from Sashi Raghupathy, who leads the Microsoft ink development team, and Jay Pittman, who leads the Microsoft handwriting recognition team. The tutorial concluded with an opportunity for researchers to give the Microsoft tablet team feedback about what they would like to see in future versions of the Tablet PC platform software. Human hair presents highly convoluted structures and spans an extraordinarily wide range of hairstyles, which is essential for the digitization of compelling virtual avatars but also one of the most challenging to create. Cutting-edge hair modeling techniques typically rely on expensive capture devices and significant manual labor. We introduce a novel data-driven framework that can digitize complete and highly complex 3D hairstyles from a single-view photograph. We first construct a large database of manually crafted hair models from several online repositories. Given a reference photo of the target hairstyle and a few user strokes as guidance, we automatically search for multiple best matching examples from the database and combine them consistently into a single hairstyle to form the large-scale structure of the hair model. We then synthesize the final hair strands by jointly optimizing for the projected 2D similarity to the reference photo, the physical plausibility of each strand, as well as the local orientation coherency between neighboring strands. We demonstrate the effectiveness and robustness of our method on a variety of hairstyles and challenging images, and compare our system with state-of-the-art hair modeling algorithms. In this paper we study the problem of hair interpolation: given two 3D hair models, we want to generate a sequence of intermediate hair models that transform from one input to another both smoothly and aesthetically pleasing. We propose an automatic method that efficiently calculates a many-to-many strand correspondence between two or more given hair models, taking into account the multi-scale clustering structure of hair. Experiments demonstrate that hair interpolation can be used for producing more vivid portrait morphing effects and enabling a novel example-based hair styling methodology, where a user can interactively create new hairstyles by continuously exploring a “style space” spanning multiple input hair models.