arXiv subjects
Publications and source records attributed to Leslie Wöhler.
We introduce a novel urban visualization system that integrates 3D urban model (CityGML) and 360° walkthrough videos. By aligning the videos with the model and dynamically projecting relevant video frames onto the geometries, our system creates photorealistic urban visualizations, allowing users to intuitively interpret geospatial data from a pedestrian view.
We propose to build realistic virtual worlds, called 360RVW, for large urban environments directly from 360° videos. We provide an interface for interactive exploration, where users can freely navigate via their own avatars. 360° videos record the entire environment of the shooting location simultaneously leading to highly realistic and immersive representations. Our system uses 360° videos recorded along streets and builds a 360RVW through four main operations: video segmentation by intersection detection, video completion to remove the videographer, semantic segmentation for virtual collision detection with the avatar, and projection onto a distorted sphere that moves along the camera trajectory following the avatar's movements. Our interface allows users to explore large urban environments by changing their walking direction at intersections or choosing a new location by clicking on a map. Even without a 3D model, the users can experience collision with buildings using metadata produced by semantic segmentation. Furthermore, we stream the 360° videos so users can directly access 360RVW via their web browser. We fully evaluate our system, including a perceptual experiment comparing our approach to previous exploratory interfaces. The results confirm the quality of our system, especially regarding the presence of users and the interactive exploration, making it most suitable for a virtual tour of urban environments.
In response to rising societal awareness of privacy concerns, face anonymization techniques have advanced, including the emergence of face-swapping methods that replace one identity with another. Achieving a balance between anonymity and naturalness in face swapping requires careful selection of identities: overly similar faces compromise anonymity, while dissimilar ones reduce naturalness. Existing models, however, focus on binary identity classification "the same person or not", making it difficult to measure nuanced similarities such as "completely different" versus "highly similar but different." This paper proposes a human-perception-based face similarity metric, creating a dataset of 6,400 triplet annotations and metric learning to predict the similarity. Experimental results demonstrate significant improvements in both face similarity prediction and attribute-based face classification tasks over existing methods.
In this work, we investigate facial anonymization techniques in 360° videos and assess their influence on the perceived realism, anonymization effect, and presence of participants. In comparison to traditional footage, 360° videos can convey engaging, immersive experiences that accurately represent the atmosphere of real-world locations. As the entire environment is captured simultaneously, it is necessary to anonymize the faces of bystanders in recordings of public spaces. Since this alters the video content, the perceived realism and immersion could be reduced. To understand these effects, we compare non-anonymized and anonymized 360° videos using blurring, black boxes, and face-swapping shown either on a regular screen or in a head-mounted display (HMD). Our results indicate significant differences in the perception of the anonymization techniques. We find that face-swapping is most realistic and least disruptive, however, participants raised concerns regarding the effectiveness of the anonymization. Furthermore, we observe that presence is affected by facial anonymization in HMD condition. Overall, the results underscore the need for facial anonymization techniques that balance both photo-realism and a sense of privacy.
Manga is a popular medium that combines stylized drawings and text to convey stories. As manga panels differ from natural images, computational systems traditionally had to be designed specifically for manga. Recently, the adaptive nature of modern large multimodal models (LMMs) shows possibilities for more general approaches. To provide an analysis of the current capability of LMMs for manga understanding tasks and identifying areas for their improvement, we design and evaluate MangaUB, a novel manga understanding benchmark for LMMs. MangaUB is designed to assess the recognition and understanding of content shown in a single panel as well as conveyed across multiple panels, allowing for a fine-grained analysis of a model's various capabilities required for manga understanding. Our results show strong performance on the recognition of image content, while understanding the emotion and information conveyed across multiple panels is still challenging, highlighting future work towards LMMs for manga understanding.
In comparison to traditional footage, 360° videos can convey engaging, immersive experiences and even be utilized to create interactive virtual environments. Like regular recordings, these videos need to consider the privacy of recorded people and could be targets for video manipulations. However, due to their properties like enhanced presence, the effects on users might differ from traditional, non-immersive content. Therefore, we are interested in how changes of real-world footage like adding privacy protection or applying video manipulations could mitigate or introduce harm in the resulting immersive media.