Prospective and retrospective reflections on avatars and virtual actors in artistic experimental exploration.
Bibliographic record
Abstract
Two researchers and artists in the Art and Technology of the Image sat down twice with the co-editors of this issue of Hybrid to explore the stakes involved regarding virtual actors and avatars based on their respective experiences in the university laboratory (INREV-AIAC at Paris 8) and with a digital art festival (Laval Virtual/Recto VRso). The first interview grapples with the dynamic that exists between art and science as it pertains to virtual protagonists in terms of their current and retrospective trajectories. While discussing avatars and virtual actors, they share their analysis regarding their expressivity, their sensitive and thoughtful natures, their human likeness, their imperfections, the empathy they illicit and how they externalise and integrate human skills. From this, the journey from the spectator’s real body to a virtual one, through avatarisation, becomes visible. In this way, their humanisation can be addressed and contrasted with our artificialisation and the emergence of a virtual co-species. The second interview is more informal, which creates a context whereby types of hybridation can be reformulated. The researchers and designers discuss the scope of artistic and creative exploration in the metaverse through avatars with the objective of being prospective in light of several issues: plural or singular identities and anticipating how behaviour and hybridisations evolve. At the heart of the debate lies the function of art and artists, between freedom and constraint, appropriation and subversion, the creation of singular worlds, referred to as “singularverses,” or the simple occupation of non-artistic spaces to test the possibilities of living together better with or through virtual creatures. virtual actors, interactive avatars, hybridisation, research and creation, technological experiments, virtual exhibition, metaverse
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.037 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".