Stylizing Face Images via Multiple Exemplars

We address the problem of transferring the style of a headshot photo to face images. Existing methods using a single exemplar lead to inaccurate results when the exemplar does not contain sufficient stylized facial components for a given photo. In this work, we propose an algorithm to stylize face images using multiple exemplars containing different subjects in the same style. Patch correspondences between an input photo and multiple exemplars are established using a Markov Random Field (MRF), which enables accurate local energy transfer via Laplacian stacks. As image patches from multiple exemplars are used, the boundaries of facial components on the target image are inevitably inconsistent. The artifacts are removed by a post-processing step using an edge-preserving filter. Experimental results show that the proposed algorithm consistently produces visually pleasing results.Comment: In CVIU 2017. Project Page: http://www.cs.cityu.edu.hk/~yibisong/cviu17/index.htm

Neural Face Editing with Intrinsic Image Disentangling

Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive. In this paper, we propose an end-to-end generative adversarial network that infers a face-specific disentangled representation of intrinsic face properties, including shape (i.e. normals), albedo, and lighting, and an alpha matte. We show that this network can be trained on "in-the-wild" images by incorporating an in-network physically-based image formation module and appropriate loss functions. Our disentangling latent representation allows for semantically relevant edits, where one aspect of facial appearance can be manipulated while keeping orthogonal properties fixed, and we demonstrate its use for a number of facial editing applications.Comment: CVPR 2017 ora

Flash Photography Enhancement via Intrinsic Relighting

We enhance photographs shot in dark environments by combining a picture taken with the available light and one taken with the flash. We preserve the ambiance of the original lighting and insert the sharpness from the flash image. We use the bilateral filter to decompose the images into detail and large scale. We reconstruct the image using the large scale of the available lighting and the detail of the flash. We detect and correct flash shadows. This combines the advantages of available illumination and flash photography.Singapore-MIT Alliance (SMA

A system for image-based modeling and photo editing

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2002.Includes bibliographical references (p. 169-178).Traditionally in computer graphics, a scene is represented by geometric primitives composed of various materials and a collection of lights. Recently, techniques for modeling and rendering scenes from a set of pre-acquired images have emerged as an alternative approach, known as image-based modeling and rendering. Much of the research in this field has focused on reconstructing and rerendering from a set of photographs, while little work has been done to address the problem of editing and modifying these scenes. On the other hand, photo-editing systems, such as Adobe Photoshop, provide a powerful, intuitive, and practical means to edit images. However, these systems are limited by their two-dimensional nature. In this thesis, we present a system that extends photo editing to 3D. Starting from a single input image, the system enables the user to reconstruct a 3D representation of the captured scene, and edit it with the ease and versatility of 2D photo editing. The scene is represented as layers of images with depth, where each layer is an image that encodes both color and depth. A suite of user-assisted tools are employed, based on a painting metaphor, to extract layers and assign depths. The system enables editing from different viewpoints, extracting and grouping of image-based objects, and modifying the shape, color, and illumination of these objects. As part of the system, we introduce three powerful new editing tools. These include two new clone brushing tools: the non-distorted clone brush and the structure-preserving clone brush. They permit copying of parts of an image to another via a brush interface, but alleviate distortions due to perspective foreshortening and object geometry.(cont.) The non-distorted clone brush works on arbitrary 3D geometry, while the structure-preserving clone brush, a 2D version, assumes a planar surface, but has the added advantage of working directly in 2D photo-editing systems that lack depth information. The third tool, a texture-illuminance decoupling filter, discounts the effect of illumination on uniformly textured areas by decoupling large- and small-scale features via bilateral filtering. This tool is crucial for relighting and changing the materials of the scene. There are many applications for such a system, for example architectural, lighting and landscape design, entertainment and special effects, games, and virtual TV sets. The system allows the user to superimpose scaled architectural models into real environments, or to quickly paint a desired lighting scheme of an interior, while being able to navigate within the scene for a fully immersive 3D experience. We present examples and results of complex architectural scenes, 360-degree panoramas, and even paintings, where the user can change viewpoints, edit the geometry and materials, and relight the environment.by Byong Mok Oh.Ph.D

Static/Dynamic Filtering for Mesh Geometry

The joint bilateral filter, which enables feature-preserving signal smoothing according to the structural information from a guidance, has been applied for various tasks in geometry processing. Existing methods either rely on a static guidance that may be inconsistent with the input and lead to unsatisfactory results, or a dynamic guidance that is automatically updated but sensitive to noises and outliers. Inspired by recent advances in image filtering, we propose a new geometry filtering technique called static/dynamic filter, which utilizes both static and dynamic guidances to achieve state-of-the-art results. The proposed filter is based on a nonlinear optimization that enforces smoothness of the signal while preserving variations that correspond to features of certain scales. We develop an efficient iterative solver for the problem, which unifies existing filters that are based on static or dynamic guidances. The filter can be applied to mesh face normals followed by vertex position update, to achieve scale-aware and feature-preserving filtering of mesh geometry. It also works well for other types of signals defined on mesh surfaces, such as texture colors. Extensive experimental results demonstrate the effectiveness of the proposed filter for various geometry processing applications such as mesh denoising, geometry feature enhancement, and texture color filtering