Fractured and fragmented selves: Queer approaches to designing virtual experiences

Virtual Creativity, Volume 13, Issue 1, Apr 2023, p. 7 - 28

This article presents an overview of three queer virtual reality projects, Virtual Drag (2016), Domestika (2017) and Untitled (2020). These are considered alongside other examples and design experiments by the authors that also take a critical, queer approach to representing bodily forms in virtual environments. This consideration includes looking at how bodies are represented in the environment that the participant explores, as well as the embodied experience of the participant visiting the environment either via a head-mounted display or via desktop. Through the discussion of queerly performed embodiment offered by these precedents, we propose a queer sensibility of disembodied presentness for virtual reality that embraces a position of immersion and presentness in multiple physical and virtual realities at once, as a theoretical and aesthetic grounding principle. Queering of presentness is a key concept that emerges through these works, in part due to the multiplicity of perspectives that VR affords. In Virtual Drag (2016) by Alison Bennett, Megan Beckwith and Mark Payne, we see the use of photogrammetry and glitch aesthetics to bring ideas of queer performativity and drag ‘realness’ to the fore when imaging bodies with permeable, porous boundaries in virtual reality. In Domestika (2017) by Jacolby Satterwhite, the virtual environment is populated with multiple avatars that repeat recordings of the artist’s dance performances; in this multiplicity of repeated traces, a viewer is unable to settle on a fixed representation of the artist’s self. This effect is amplified by the participant’s non-corporeal presence, demonstrating the creative potential of queer disorientation in virtual environments. In Untitled (2020) by Tarik Ahlip and Tactical Space Lab, we are immersed in a cinematic environment, filled with simulated light. There, we encounter the artist’s body, disembodied and forcing the viewer into the role of virtual voyeur and onlooker to actions out of time.

DOI: https://doi.org/10.1386/vcr_00072_1

Silent version (2018).

A screen recording of a live watch-through of Stalker(1979) as interpreted by MobileNet (using the ml5.js library). A contemplative viewing reveals some of the extents and limitations of the world/space within the model trained on the ImageNet database.

Creating conversations between myself (with insight into the protagonist's world model within the written text) and characters in "Twice, again", has brought up questions of how aspects of the ongoing conversation are remembered, what should be remembered and, perhaps more importantly, what it is okay, or even necessary to forget.

"I thought that each of my words (that each of my movements) would persist in his implacable memory; I was benumbed by the fear of multiplying useless gestures."

Funes The Memorious, Borges

The context of the current conversation, what is remembered & what is forgotten — is by necessity actively maintained, it is the only thing setting it apart from the "collective memory" of the model itself.

Reanbar is here...

Direct link to youtube: youtu.be/EoMl2SiG8i8

The overGround:underStory project provides a platform for knowing through doing. A large part of this doing takes the form of the invitation we made to "slow down, observe ,record, ask questions and share" as part of the Urban Field Naturalist Project. How do we come to an understanding of the networks that are now woven between the physical and digital of more-than-human ecologies through centering these steps in creative practice based research?

Thinking critically about the technologies we are presented with as being 'inevitable' involves spending time with them in order to come to a deeper understanding of them. "What Raven Saw" is then described as a "more-than-human collaboration between myself and my silicon & carbon kin, including wombats, the local ravens & other bird-life, the GPT-3 large language model & the Stable Diffusion Model." This is a description that should be read critically and along side an understanding of the dangerous and unethical framing, positioning and development of these technologies (and the people behind them). [A brief outline of some of the networks involved here, and the money/power structrues involved - from Timnit Gebru in Wired] Part of the knowing through doing then is to prioritise how to not lose site of this critical approach when working directly with the systems under the critical lens.

video documentation for the spoken word / slide performance of "What Raven Saw [an] overGround:underStory". Performed under the 48:9 screen at UTS Central (Broadway, Sydney) for the School of Design's, Visual Knowledges research group's - "Visualising the Invisible: Image making in climate communication symposium".

I have seen the noise created in the Neural Radiance Field generation process as "hallucinations". Language that is both poetic & problematic at the same time. Rather than attempting to reduce this visual phenomena, I have been working with it (and finding ways to encourage / exaggerate it) as a way of embracing and exploring this NeRF aesthetic. (An aesthetic I assume will be optimised out of the process as the technology "advances")

["The Nicholson Hermes" @ the Chau Chak Wing Museum ]

NOTES

Hallucination is currently in common usage across the machine learning space as an accepted "semi-technical" term, including in the "mission" page for Meta's ill-fated(?) "Galactica".

image: https://github.com/NVlabs/instant-ngp/blob/master/docs/nerf_dataset_tips.md

image: https://galactica.org/mission/

Trailer for the spoken word / slide performance of "What Raven Saw [an] overGround:underStory". Performed under the 48:9 screen at UTS Central (Broadway, Sydney) for the School of Design's, Visual Knowledges research group's - "Visualising the Invisible: Image making in climate communication symposium".

The visuals depict an excerpt from the performance exploring co-imagined (with a machine learning diffusion model) unremembered transitory forms of now-recognisable species.