Browsing by Author "Steed, Anthony"

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    Impact of Immersiveness on Persuasiveness, Politeness, and Social Adherence in Human-Agent Interactions within Small Groups
    (The Eurographics Association, 2023) Zojaji, Sahba; Steed, Anthony; Peters, Christopher; Jean-Marie Normand; Maki Sugimoto; Veronica Sundstedt
    Politeness is critical for shaping human-human interactions and therefore seems an important consideration in human interactions with Embodied Conversational Agents (ECAs). However, the impact of artificially-generated politeness behaviors on humans in Virtual Environments (VE) is not clear. We explore the impact of immersiveness on the perceived politeness and consequent persuasive abilities of ECAs in a small group context. A user study with two main conditions, immersive and nonimmersive, was conducted with 66 participants. In the immersive condition, participants were fully immersed in virtual reality (HMD, walking freely), while in the non-immersive condition, participants used a desktop computer interface (screen display, mouse and keyboard control). In both conditions, the primary agent in a group of two ECAs invited participants to join the group using six politeness behaviors derived from Brown and Levinson's politeness theory. While the results of the study did not indicate any significant differences between the immersive and non-immersive conditions in terms of persuasiveness and offensiveness, in the immersive condition, participants perceived the ECAs as less friendly and found their requests to be less clear. On the other hand, participants in the immersive condition reported a greater sense of freedom. Furthermore, the nonimmersive condition showed higher adherence to social norms compared to the immersive condition. These findings emphasize the significance of examining immersiveness on the persuasiveness of ECAs and their perceived politeness and social adherence by humans in human-agent interactions within small groups.
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    Oetermining orientation of Laser scanned surfaces
    (The Eurographics Association, 2022) Oliveira, João Fradinho; Steed, Anthony; Xavier Pueyo; Manuel Próspero dos Santos; Luiz Velho
    Real 3D data acquired from scanning technology provide interesting 3D models for research and industrial applications. However before these models can be used, a surface needs to be jitted to a point cloud of an unknown object, this process might create some undesirable properties, such as triangle normais pointing in incorrect directions. We present a robust algorithm that reliably fixes these triangle normal problems on nonmanifold, and self-interesting surfaces of scanned objects.
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    Selecting Texture Resolution Using a Task-specific Visibility Metric
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Wolski, Krzysztof; Giunchi, Daniele; Kinuwaki, Shinichi; Didyk, Piotr; Myszkowski, Karol; Steed, Anthony; Mantiuk, Rafal K.; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    In real-time rendering, the appearance of scenes is greatly affected by the quality and resolution of the textures used for image synthesis. At the same time, the size of textures determines the performance and the memory requirements of rendering. As a result, finding the optimal texture resolution is critical, but also a non-trivial task since the visibility of texture imperfections depends on underlying geometry, illumination, interactions between several texture maps, and viewing positions. Ideally, we would like to automate the task with a visibility metric, which could predict the optimal texture resolution. To maximize the performance of such a metric, it should be trained on a given task. This, however, requires sufficient user data which is often difficult to obtain. To address this problem, we develop a procedure for training an image visibility metric for a specific task while reducing the effort required to collect new data. The procedure involves generating a large dataset using an existing visibility metric followed by refining that dataset with the help of an efficient perceptual experiment. Then, such a refined dataset is used to retune the metric. This way, we augment sparse perceptual data to a large number of per-pixel annotated visibility maps which serve as the training data for application-specific visibility metrics. While our approach is general and can be potentially applied for different image distortions, we demonstrate an application in a game-engine where we optimize the resolution of various textures, such as albedo and normal maps.