Enhanced Shadow Retargeting with Light-Source Estimation Using Flat Fresnel Lenses

Shadow-retargeting maps depict the appearance of real shadows to virtual shadows given corresponding deformation of scene geometry, such that appearance is seamlessly maintained. By performing virtual shadow reconstruction from unoccluded real-shadow samples observed in the camera frame, this method efficiently recovers deformed shadow appearance. In this manuscript, we introduce a light-estimation approach that enables light-source detection using flat Fresnel lenses that allow this method to work without a set of pre-established conditions. We extend the adeptness of this approach by handling scenarios with multiple receiver surfaces and a non-grounded occluder with high accuracy. Results are presented on a range of objects, deformations, and illumination conditions in real-time Augmented Reality (AR) on a mobile device. We demonstrate the practical application of the method in generating otherwise laborious in-betweening frames for 3D printed stop-motion animatio

Saliency-aware Stereoscopic Video Retargeting

Stereo video retargeting aims to resize an image to a desired aspect ratio. The quality of retargeted videos can be significantly impacted by the stereo videos spatial, temporal, and disparity coherence, all of which can be impacted by the retargeting process. Due to the lack of a publicly accessible annotated dataset, there is little research on deep learning-based methods for stereo video retargeting. This paper proposes an unsupervised deep learning-based stereo video retargeting network. Our model first detects the salient objects and shifts and warps all objects such that it minimizes the distortion of the salient parts of the stereo frames. We use 1D convolution for shifting the salient objects and design a stereo video Transformer to assist the retargeting process. To train the network, we use the parallax attention mechanism to fuse the left and right views and feed the retargeted frames to a reconstruction module that reverses the retargeted frames to the input frames. Therefore, the network is trained in an unsupervised manner. Extensive qualitative and quantitative experiments and ablation studies on KITTI stereo 2012 and 2015 datasets demonstrate the efficiency of the proposed method over the existing state-of-the-art methods. The code is available at https://github.com/z65451/SVR/.Comment: 8 pages excluding references. CVPRW conferenc

Intermediated reality

Real-time solutions to reducing the gap between virtual and physical worlds for photorealistic interactive Augmented Reality (AR) are presented. First, a method of texture deformation with image inpainting, provides a proof of concept to convincingly re-animate fixed physical objects through digital displays with seamless visual appearance. This, in combination with novel methods for image-based retargeting of real shadows to deformed virtual poses and environment illumination estimation using in conspicuous flat Fresnel lenses, brings real-world props to life in compelling, practical ways. Live AR animation capability provides the key basis for interactive facial performance capture driven deformation of real-world physical facial props. Therefore, Intermediated Reality (IR) is enabled; a tele-present AR framework that drives mediated communication and collaboration for multiple users through the remote possession of toys brought to life.This IR framework provides the foundation of prototype applications in physical avatar chat communication, stop-motion animation movie production, and immersive video games. Specifically, a new approach to reduce the number of physical configurations needed for a stop-motion animation movie by generating the in-between frames digitally in AR is demonstrated. AR-generated frames preserve its natural appearance and achieve smooth transitions between real-world keyframes and digitally generated in-betweens. Finally, the methods integrate across the entire Reality-Virtuality Continuum to target new game experiences called Multi-Reality games. This gaming experience makes an evolutionary step toward the convergence of real and virtual game characters for visceral digital experiences

A deep evaluator for image retargeting quality by geometrical and contextual interaction

An image is compressed or stretched during the multidevice displaying, which will have a very big impact on perception quality. In order to solve this problem, a variety of image retargeting methods have been proposed for the retargeting process. However, how to evaluate the results of different image retargeting is a very critical issue. In various application systems, the subjective evaluation method cannot be applied on a large scale. So we put this problem in the accurate objective-quality evaluation. Currently, most of the image retargeting quality assessment algorithms use simple regression methods as the last step to obtain the evaluation result, which are not corresponding with the perception simulation in the human vision system (HVS). In this paper, a deep quality evaluator for image retargeting based on the segmented stacked AutoEnCoder (SAE) is proposed. Through the help of regularization, the designed deep learning framework can solve the overfitting problem. The main contributions in this framework are to simulate the perception of retargeted images in HVS. Especially, it trains two separated SAE models based on geometrical shape and content matching. Then, the weighting schemes can be used to combine the obtained scores from two models. Experimental results in three well-known databases show that our method can achieve better performance than traditional methods in evaluating different image retargeting results