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Record W3160273274 · doi:10.1109/vrw52623.2021.00169

bmlSUP – A SMPL Unity Player

2021· article· en· W3160273274 on OpenAlexaff
Adam O. Bebko, Anne Thaler, Nikolaus F. Troje

Bibliographic record

Venue2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · 2021
Typearticle
Languageen
FieldEngineering
Topic3D Shape Modeling and Analysis
Canadian institutionsYork University
Fundersnot available
KeywordsComputer scienceVirtual realityHuman–computer interactionAnimationComputer graphics (images)

Abstract

fetched live from OpenAlex

Realistic virtual characters are important for many applications. The SMPL body model is based on 3D body scans and uses body shape and pose-dependent blendshapes to achieve realistic human animations [3]. Recently, a large database of SMPL animations called AMASS has been released [4]. Here, we present a tool that allows these animations to be viewed and controlled in Unity called the BioMotionLab SMPL Unity Player (bmlSUP). This tool provides an easy interface to load and display AMASS animations in 3D immersive environments and mixed reality. We present the functionality, uses, and possible applications of this new tool.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.272
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

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