The demand for privacy in facial image dissemination is gaining ground internationally, echoed by the proliferation of regulations such as GDPR, DPDPA, CCPA, PIPL, and APPI. While recent advances in anonymization surpass pixelation or blur methods, additional constraints to the task pose challenges. Largely unaddressed by current anonymization methods are clinical images and pairs of before-and-after clinical images illustrating facial medical interventions, e.g., facial surgeries or dental procedures. We present VerA, the first Versatile Anonymization framework that solves two challenges in clinical applications: A) it preserves selected semantic areas (e.g., mouth region) to show medical intervention results, that is, anonymization is only applied to the areas outside the preserved area; and B) it produces anonymized images with consistent personal identity across multiple photographs, which is crucial for anonymizing photographs of the same person taken before and after a clinical intervention. We validate our results on both single and paired anonymization of clinical images through extensive quantitative and qualitative evaluation. We also demonstrate that VerA reaches the state of the art on established anonymization tasks, in terms of photorealism and de-identification.

Automatic news articles clustering is one of the most important tasks for news publishers. Traditional unsupervised models exploit generic text representation (e.g., BERT) and typically do not consider the relationships between each paragraph in news articles. Such depth learning from news articles is important for clustering full-length articles. Recently contrastive learning (CL) has shown to be a popular method for representation learning that uses positive and negative data pairs generated using data augmentation techniques to improve the representation in the latent space. In this work, we propose text augmentation methods and use contrastive learning to cluster daily growing full-length German news articles. Our experiments on four German news article datasets (one labeled and three unlabeled datasets) demonstrate that contrastive learning and our text augmentation methods significantly improve the representation of news articles compared to generic pre-trained text representation and have high performance for clustering tasks.

The rapid growth in the number of news articles published daily can create challenges for users to explore specific topics and gather different perspectives around the topics to make neutral and unbiased conclusions. The system's ability to intelligently cluster news articles from multiple sources and retrieve concise (pro/con) relevant arguments is necessary for users' well-informed decision-making. In this paper, we introduce our unified argument retrieval system that uses our clustering model to cluster news articles and subsequently extracts the core arguments from news articles using the argument prediction model. We conducted a user study to understand the system's usability and users' satisfaction with the quality of clusters and arguments extracted.

This demo explores the use of computer vision technologies for the integration of scent in video games and interactive applications. We present an extendable system that is domain-independent and allows for customization and debugging based on the targeted game. Using Minecraft as a case study, we optimized the system configuration and evaluated its performance. Our aim is to advance the exploration of scent integration in gaming and inspire future designs for olfactory experiences.

Attractive images or videos are the visual backbones of journalism and social media to gain the user's attention. From trailers to teaser images to image galleries, appealing visuals have only grown in importance over the years. However, selecting eye-catching shots from a video or the perfect image from large image collections is a challenging and time-consuming task. We present our tool that can assess image and video content from an aesthetic standpoint. We discovered that it is possible to perform such an assessment by combining expert knowledge with data-driven information. We combine the relevant aesthetic features and machine learning algorithms into an aesthetics retrieval system, which enables users to sort uploaded visuals based on an aesthetic score and interact with additional photographic, cinematic, and person-specific features.

When digital applications aim to blend virtual and real worlds, understanding the actual physical actions of users becomes an important task; the precise timing of these tangible interaction events is needed, along with the identity, and possibly location and history, of all involved actors/objects. With multiple actors or objects, it is difficult to identify who touches which object and when. Instrumenting objects for Body Channel Communication (BCC) allows message exchange around the human body between instrumented objects and the user themselves. In this paper we show how BCC can be utilized to perform under real-time conditions so that we can directly notice touch events (and the identity of actors). TangibleID is a framework that unifies tangible interaction capture for objects and users based on wearable BCC. TangibleID provides identification and communication with tagged objects/users in less than 120 ms and supports a variety of tangible interactions, without the need to restrict user (hand) movements or to maintain line-of-sight connection to cameras. When an AR application is combined with TangibleID, a new tangible mixed reality experience is achieved, as demonstrated in the “Haunted Castle” showcase. The paper presents an end-to-end technical evaluation including trade-offs regarding robustness and speed of touch recognition, outlines the breadth of interaction modalities, and reports on an initial user assessment.

This paper explores new authoring paradigms and computer-assisted authoring tools for free-form interactive narratives. We present a new design formalism, Interactive Behavior Trees (IBT's), which decouples the monitoring of user input, the narrative, and how the user may influence the story outcome. We introduce automation tools for IBT's, to help the author detect and automatically resolve inconsistencies in the authored narrative, or conflicting user interactions that may hinder story progression. We compare IBT's to traditional story graph representations and show that our formalism better scales with the number of story arcs, and the degree and granularity of user input. The authoring time is further reduced with the help of automation, and errors are completely avoided. Our approach enables content creators to easily author complex, branching narratives with multiple story arcs in a modular, extensible fashion while empowering players with the agency to freely interact with the characters in the story and the world they inhabit.

Coloring books capture the imagination of children and provide them with one of their earliest opportunities for creative expression. However, given the proliferation and popularity of digital devices, real-world activities like coloring can seem unexciting, and children become less engaged in them. Augmented reality holds unique potential to impact this situation by providing a bridge between real-world activities and digital enhancements. In this paper, we present an augmented reality coloring book App in which children color characters in a printed coloring book and inspect their work using a mobile device. The drawing is detected and tracked, and the video stream is augmented with an animated 3-D version of the character that is textured according to the child's coloring. This is possible thanks to several novel technical contributions. We present a texturing process that applies the captured texture from a 2-D colored drawing to both the visible and occluded regions of a 3-D character in real time. We develop a deformable surface tracking method designed for colored drawings that uses a new outlier rejection algorithm for real-time tracking and surface deformation recovery. We present a content creation pipeline to efficiently create the 2-D and 3-D content. And, finally, we validate our work with two user studies that examine the quality of our texturing algorithm and the overall App experience.

We investigate the influence of motion effects in the domain of mobile Augmented Reality (AR) games on user experience and task performance. The work focuses on evaluating responses to a selection of synthesized camera oriented reality mixing techniques for AR, such as motion blur, defocus blur, latency and lighting responsiveness. In our cross section of experiments, we observe that these measures have a significant impact on perceived realism, where aesthetic quality is valued. However, lower latency records the strongest correlation with improved subjective enjoyment, satisfaction, and realism, and objective scoring performance. We conclude that the reality mixing techniques employed are not significant in the overall user experience of a mobile AR game, except where harmonious or convincing blended AR image quality is consciously desired by the participants.

The facial performance of an individual is inherently rich in subtle deformation and timing details. Although these subtleties make the performance realistic and compelling, they often elude both motion capture and hand animation. We present a technique for adding fine-scale details and expressiveness to low-resolution art-directed facial performances, such as those created manually using a rig, via marker-based capture, by fitting a morphable model to a video, or through Kinect reconstruction using recent faceshift technology. We employ a high-resolution facial performance capture system to acquire a representative performance of an individual in which he or she explores the full range of facial expressiveness. From the captured data, our system extracts an expressiveness model that encodes subtle spatial and temporal deformation details specific to that particular individual. Once this model has been built, these details can be transferred to low-resolution art-directed performances. We demonstrate results on various forms of input; after our enhancement, the resulting animations exhibit the same nuances and fine spatial details as the captured performance, with optional temporal enhancement to match the dynamics of the actor. Finally, we show that our technique outperforms the current state-of-the-art in example-based facial animation.

In this paper we propose a novel method to compress video content based on image retargeting. First, a saliency map is extracted from the video frames either automatically or according to user input. Next, nonlinear image scaling is performed which assigns a higher pixel count to salient image regions and fewer pixels to non-salient regions. The non-linearly downscaled images can then be compressed using existing compression techniques and decoded and upscaled at the receiver. To this end we introduce a non-uniform antialiasing technique that significantly improves the image resampling quality. The overall process is complementary to existing compression methods and can be seamlessly incorporated into existing pipelines. We compare our method to JPEG 2000 and H.264/AVC-10 and show that, at the cost of visual quality in non-salient image regions, our method achieves a significant improvement of the visual quality of salient image regions in terms of Structural Similarity (SSIM) and Peak Signal-to-Noise-Ratio (PSNR) quality measures, in particular for scenarios with high compression ratios.