Single-image RGB Photometric Stereo With Spatially-varying Albedo

We present a single-shot system to recover surface geometry of objects with spatially-varying albedos, from images captured under a calibrated RGB photometric stereo setup---with three light directions multiplexed across different color channels in the observed RGB image. Since the problem is ill-posed point-wise, we assume that the albedo map can be modeled as piece-wise constant with a restricted number of distinct albedo values. We show that under ideal conditions, the shape of a non-degenerate local constant albedo surface patch can theoretically be recovered exactly. Moreover, we present a practical and efficient algorithm that uses this model to robustly recover shape from real images. Our method first reasons about shape locally in a dense set of patches in the observed image, producing shape distributions for every patch. These local distributions are then combined to produce a single consistent surface normal map. We demonstrate the efficacy of the approach through experiments on both synthetic renderings as well as real captured images.Comment: 3DV 2016. Project page at http://www.ttic.edu/chakrabarti/rgbps

Models of Visual Appearance for Analyzing and Editing Images and Videos

The visual appearance of an image is a complex function of factors such as scene geometry, material reflectances and textures, illumination, and the properties of the camera used to capture the image. Understanding how these factors interact to produce an image is a fundamental problem in computer vision and graphics. This dissertation examines two aspects of this problem: models of visual appearance that allow us to recover scene properties from images and videos, and tools that allow users to manipulate visual appearance in images and videos in intuitive ways. In particular, we look at these problems in three different applications. First, we propose techniques for compositing images that differ significantly in their appearance. Our framework transfers appearance between images by manipulating the different levels of a multi-scale decomposition of the image. This allows users to create realistic composites with minimal interaction in a number of different scenarios. We also discuss techniques for compositing and replacing facial performances in videos. Second, we look at the problem of creating high-quality still images from low-quality video clips. Traditional multi-image enhancement techniques accomplish this by inverting the camera’s imaging process. Our system incorporates feature weights into these image models to create results that have better resolution, noise, and blur characteristics, and summarize the activity in the video. Finally, we analyze variations in scene appearance caused by changes in lighting. We develop a model for outdoor scene appearance that allows us to recover radiometric and geometric infor- mation about the scene from images. We apply this model to a variety of visual tasks, including color-constancy, background subtraction, shadow detection, scene reconstruction, and camera geo-location. We also show that the appearance of a Lambertian scene can be modeled as a combi- nation of distinct three-dimensional illumination subspaces — a result that leads to novel bounds on scene appearance, and a robust uncalibrated photometric stereo method.Engineering and Applied Science

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

Example-based video color grading

In most professional cinema productions, the color palette of the movie is painstakingly adjusted by a team of skilled colorists -- through a process referred to as color grading -- to achieve a certain visual look. The time and expertise required to grade a video makes it difficult for amateurs to manipulate the colors of their own video clips. In this work, we present a method that allows a user to transfer the color palette of a model video clip to their own video sequence. We estimate a per-frame color transform that maps the color distributions in the input video sequence to that of the model video clip. Applying this transformation naively leads to artifacts such as bleeding and flickering. Instead, we propose a novel differential-geometry-based scheme that interpolates these transformations in a manner that minimizes their curvature, similarly to curvature flows. In addition, we automatically determine a set of keyframes that best represent this interpolated transformation curve, and can be used subsequently, to manually refine the color grade. We show how our method can successfully transfer color palettes between videos for a range of visual styles and a number of input video clips.Engineering and Applied Science

Deep Image Harmonization

Compositing is one of the most common operations in photo editing. To generate realistic composites, the appearances of foreground and background need to be adjusted to make them compatible. Previous approaches to harmonize composites have focused on learning statistical relationships between hand-crafted appearance features of the foreground and background, which is unreliable especially when the contents in the two layers are vastly different. In this work, we propose an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization. We also introduce an efficient way to collect large-scale and high-quality training data that can facilitate the training process. Experiments on the synthesized dataset and real composite images show that the proposed network outperforms previous state-of-the-art methods