Raypointing, the status-quo pointing technique for virtual reality, is challenging with many occluded and overlapping objects. In this work, we investigate how eye-tracking input can assist the gestural ray pointing in the disambiguation of targets in densely populated scenes. We explore the concept of Gaze + Plane, where the intersection between the user's gaze and a hand-controlled plane facilitates 3D position specification. In particular, two techniques are investigated: Gaze&Wall, which employs an indirect plane positioned in depth using a hand ray, and Gaze&Racket, featuring a hand-held and rotatable plane. In a first experiment, we reveal the speed-error trade-offs between Gaze + Plane techniques. In a second study, we compared the best techniques to newly designed gesture-only techniques, finding that Gaze&Wall is less error-prone and significantly faster. Our research has relevance for spatial interaction, specifically on advanced techniques for complex 3D tasks.

Peripheral vision plays a significant role in human perception and orientation. However, its relevance for human-computer interaction, especially head-mounted displays, has not been fully explored yet. In the past, a few specialized appliances were developed to display visual cues in the periphery, each designed for a single specific use case only. A multi-purpose headset to exclusively augment peripheral vision did not exist yet. We introduce MoPeDT: Modular Peripheral Display Toolkit, a freely available, flexible, reconfigurable, and extendable headset to conduct peripheral vision research. MoPeDT can be built with a 3D printer and off-the-shelf components. It features multiple spatially configurable near-eye display modules and full 3D tracking inside and outside the lab. With our system, researchers and designers may easily develop and prototype novel peripheral vision interaction and visualization techniques. We demonstrate the versatility of our headset with several possible applications for spatial awareness, balance, interaction, feedback, and notifications. We conducted a small study to evaluate the usability of the system. We found that participants were largely not irritated by the peripheral cues, but the headset's comfort could be further improved. We also evaluated our system based on established heuristics for human-computer interaction toolkits to show how MoPeDT adapts to changing requirements, lowers the entry barrier for peripheral vision research, and facilitates expressive power in the combination of modular building blocks.

The placement of vegetation plays a central role in the realism of virtual scenes. We introduce procedural placement models (PPMs) for vegetation in urban layouts. PPMs are environmentally sensitive to city geometry and allow identifying plausible plant positions based on structural and functional zones in an urban layout. PPMs can either be directly used by defining their parameters or learned from satellite images and land register data. This allows us to populate urban landscapes with complex 3D vegetation and enhance existing approaches for generating urban landscapes. Our framework’s effectiveness is shown through examples of large-scale city scenes and close-ups of individually grown tree models. We validate the results generated with our framework with a perceptual user study and its usability based on urban scene design sessions with expert users.

Adapting the resolution of motion compensated prediction in a video codec is considered in this paper. A new motion search and signaling scheme for increasing the accuracy of prediction without affecting the complexity of encoding is proposed. It involves using common information available to both encoder and decoder, e.g. current slice type, texture in reference pictures, etc as a cue to control motion vector accuracy and an efficient reuse of motion information for predicting subsequent blocks. An average bit rate reduction of around 2.5% for P-pictures and 0.5% for B-pictures is observed with no extra search compared to a fixed quarter-sample resolution.