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Record W4400814018 · doi:10.1145/3664221

Lightning Artist Toolkit: A Hand-Drawn Volumetric Animation Pipeline

2024· article· en· W4400814018 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the ACM on Computer Graphics and Interactive Techniques · 2024
Typearticle
Languageen
FieldEngineering
Topic3D Shape Modeling and Analysis
Canadian institutionsYork University
FundersUniversitas Brawijaya
KeywordsAnimationComputer scienceUSablePipeline (software)Process (computing)Computer animationComputer facial animationPoint (geometry)GestureSet (abstract data type)Action (physics)Computer graphics (images)Point cloudLightning (connector)Human–computer interactionArtificial intelligenceMultimedia

Abstract

fetched live from OpenAlex

We propose a set of methods for freely integrating live-action volumetric video with hand-drawn volumetric animation, which our research develops as the Lightning Artist Toolkit (Latk)---a complete pipeline for hand-drawn volumetric animation, as far as we know the only open-source example of its kind. Our goal with this project is to make creation in 3D as expressive and intuitive as creation in 2D, retaining the human gesture from its origins in hand-drawn animation on paper. This effort is less a computer vision challenge with an objective goal, as with for example point cloud segmentation, than it is an attempt to approximate human vision---a drawing process that records only information from a scene that was subjectively important to an individual artist. In addition to supporting animation efforts in the near term, we believe the public TiltSet dataset assembled for this project will remain usable in new and unexpected ways.

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.

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 categoriesnone
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 score0.862
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.236
Teacher spread0.225 · 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