Perception Manipulation for Seamless Face-to-face Remote Collaboration

access_time March 31, 2020 at 02:00PM até March 31, 2020 at 04:00PM
place https://videoconf-colibri.zoom.us/j/199216208

This thesis aims to improve remote collaboration in shared 3D workspaces. Current mixed reality technologies allow geographically distant collaborators to be together and share the samevirtual space, making it possible for people to see each other through realistic virtual representations. Face-to-face telepresence also promotes a sense of presence and can improve collaboration by allowing immediate understanding of nonverbal cues. Indeed, several approaches have successfully explored face-to-face remote interactions with 2D content. However, when collaborating in a 3D object-centered volumetric workspace, there is a decrease in awareness due to gesture ambiguities, occlusions, and different participants’ viewpoints. In this dissertation, we contribute the use of perception manipulation to improve workspace awareness in computer-supported collaborative work in mixed reality telepresence environments by assuring that remote collaborators are always aware of what is happening in the workspace when communicating using nonverbal cues. We began by contributing the technological foundations to prototype remote interactions. And then, we proposed and evaluated perception manipulation techniques focused on allowing remote people always to share the same understanding of the workspace. And, at the same time, being aware of nonverbal communication. Results suggest that by purposefully changing the properties of the person-task space using geometric transformations, warping, and repositioning devices, we can counteract gesture ambiguities, eliminate workspace occlusions, and promote a shared understanding of the workspace. In conclusion, we have validated our thesis, stating that perception manipulation techniques increase workspace awareness and improve face-to-face remote collaboration in mixed reality 3D workspaces.

local_offer Doctoral exam
person Candidato: António Maurício Lança Tavares de Sousa
supervisor_account Orientador: Prof. Joaquim Armando Pires Jorge