Exploring Virtual Bodies and Invisible Avatars as Storytelling Tools in Contemporary Narrative-Based Virtual Reality Projects
Bibliographic record
Abstract
Contemporary narrative-based virtual reality (VR) projects engage with interactors in a variety of ways. While some projects offer a virtual body or avatar that is physically manifested, other projects might position the user as an invisible body within a 360-degree story world or offer them virtual hands that are operated by hand controllers. This presentation explores the narrative impacts of these varying representations, and the way that they foster a sense of embodiment for the interactor. Moreover, the presentation explores the ways that the interactor’s body, as a physically manifested or invisible avatar, might be harnessed as a storytelling tool to evoke or advance a narrative. Scholars across various disciplines, including psychology, narratology, and computer science, have provided converging perspectives on VR’s ability to generate feelings of 'presence' and 'immersion.' Generally, these terms refer to a medium's capacity to position users within a dynamic spatial and potentially social environment that is distinct from the 'real' world. Regarding VR, Riva et al. note that, ‘there is consensus that the experience of presence is a complex, multidimensional perception, formed through an interplay of raw (multi-) sensory data and various cognitive processes’ (2007, p. 46). Further to the psychological state of presence, the concept of embodiment captures the corporeal experience of feeling present in a virtual environment. Biocca (1997) suggests that embodiment leads to 'self-presence,' involving salient mental models of the self within a virtual setting. In the context of contemporary narrative-based VR projects, advanced graphics and head tracking technologies contribute to this sense of self- presence, as well as embodiment and immersion in contemporary VR projects’ realistically or imaginatively rendered story worlds. This area is explored in this presentation via a close reading and phenomenological analysis of three recent narrative-based VR projects. These projects offer varying bodily representations and expressions of agency for the participant. The presentation interrogates the interactive devices and stylistic features of the three projects, noting how these allow the interactor to participate in the unfolding narratives. This analysis is guided by Kilteri et al.'s definition of sense of embodiment (SoE) in virtual reality (2012). This contends that a SoE can consist of three subcomponents: sense of self-location, sense of agency, and sense of body ownership (p. 375-377). Ultimately, the presentation will argue that the specificity of VR as a storytelling format is its emphasis on embodied interactions between users, represented characters, and other story agents. This is particularly evident in works featuring a digital body representation or avatar that serves as a surrogate for the interactor's 'real' body. The digital avatar empowers the interactor to shape the narrative through bodily movements and interactions with story agents, allowing real-time actions to meaningfully influence a narrative's progression. However, the presentation emphasizes that a virtual body representation or avatar is not a prerequisite for a powerful corporeal engagement with a VR work. Projects positioning the interactor as an unseen observer can also leverage their physical body as a storytelling tool by necessitating user interaction with and contemplation of a scene.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".