We present a method for compositing virtual objects into a photograph such that the object colors appear to have been processed by the photo's camera imaging pipeline. Compositing in such a camera-aware manner is essential for high realism, and it requires the color transformation in the photo's pipeline to be inferred, which is challenging due to the inherent one-to-many mapping that exists from a scene to a photo. To address this problem for the case of a single photo taken from an unknown camera, we propose a dual-learning approach in which the reverse color transformation (from the photo to the scene) is jointly estimated. Learning of the reverse transformation is used to facilitate learning of the forward mapping, by enforcing cycle consistency of the two processes. We additionally employ a feature sharing schema to extract evidence from the target photo in the reverse mapping to guide the forward color transformation. Our dual-learning approach achieves object compositing results that surpass those of alternative techniques.
In video see-through augmented reality (AR), virtual objects are overlaid over digital video images. One particular problem of this image mixing process is that the visual appearance of the computer graphics differs strongly from the real background image. The reason for this is that typical AR systems use fast but simple real-time rendering techniques for displaying virtual objects. In this paper, methods for reducing the impact of three effects which make virtual and real objects easily distinguishable are presented. The first effect is camera image noise, which is contained in the data delivered by the image sensor used for capturing the real scene. The second effect considered is edge aliasing, which makes distinguishing virtual objects from real objects simple. Finally, we consider motion blur, which is caused by the temporal integration of color intensities in the image sensor during fast movements of the camera or observed objects. In this paper, we present a system for generating a realistic simulation of image noise based on a new camera calibration step. Additionally, a rendering algorithm is introduced, which performs a smooth blending between the camera image and virtual objects at their boundary in order to reduce aliasing. Lastly, a rendering method is presented, which produces motion blur according to the current camera movement. The implementation of the new rendering techniques utilizes the programmability of modern graphics processing units (GPUs) and delivers real-time frame rates. We present a method for estimating the real-world lighting conditions within a scene in real-time. The estimation is based on the visual appearance of a human face in the real scene captured in a single image of a monocular camera. In hardware setups featuring a user-facing camera, an image of the user’s face can be acquired at any time. The limited range in variations between different human faces makes it possible to analyze their appearance offline, and to apply the results to new faces. Our approach uses radiance transfer functions – learned offline from a dataset of images of faces under different known illuminations – for particular points on the human face. Based on these functions, we recover the most plausible real-world lighting conditions for measured reflections in a face, represented by a function depending on incident light angle using Spherical Harmonics. The pose of the camera relative to the face is determined by means of optical tracking, and virtual 3D content is rendered and overlaid onto the real scene with a fixed spatial relationship to the face. By applying the estimated lighting conditions to the rendering of the virtual content, the augmented scene is shaded coherently with regard to the real and virtual parts of the scene. We show with different examples under a variety of lighting conditions, that our approach provides plausible results, which considerably enhance the visual realism in real-time Augmented Reality applications. We propose a method to realistically insert synthetic objects into existing photographs without requiring access to the scene or any additional scene measurements. With a single image and a small amount of annotation, our method creates a physical model of the scene that is suitable for realistically rendering synthetic objects with diffuse, specular, and even glowing materials while accounting for lighting interactions between the objects and the scene. We demonstrate in a user study that synthetic images produced by our method are confusable with real scenes, even for people who believe they are good at telling the difference. Further, our study shows that our method is competitive with other insertion methods while requiring less scene information. We also collected new illumination and reflectance datasets; renderings produced by our system compare well to ground truth. Our system has applications in the movie and gaming industry, as well as home decorating and user content creation, among others. Video see-through Augmented Reality adds computer graphics to the real world in real time by overlaying graphics onto a live video feed. To achieve a realistic integration of the virtual and real imagery, the rendered images should have a similar appearance and quality to those produced by the video camera. This paper describes a compositing method which models the artifacts produced by a small low-cost camera, and adds these effects to an ideal pinhole image produced by conventional rendering methods. We attempt to model and simulate each step of the imaging process, including distortions, chromatic aberrations, blur, Bayer masking, noise, sharpening, and color-space compression, all while requiring only an RGBA image and an estimate of camera velocity as inputs.