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Record W6926368663 · doi:10.2312/hpg.20251168

Real-Time GPU Tree Generation

2025· article· en· W6926368663 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

VenueEurographics · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsRendering (computer graphics)Polygon (computer graphics)Tree (set theory)Real-time renderingProcedural modelingTree structure

Abstract

fetched live from OpenAlex

Trees for real-time media are typically created using procedural algorithms and then baked to a polygon format, requiring large amounts of memory. We propose a novel procedural system and model for generating and rendering realistic trees and similar vegetation specifically tailored to run in real-time on GPUs. By using GPU work graphs with mesh nodes, we render gigabytes-worth of tree geometry from kilobytes of generation code every frame exclusively on the GPU. Contrary to prior work, our method combines instant in-engine artist authoring, continuous frame-specific level of detail and tessellation, highly detailed animation, and seasonal details like blossoms, fruits, and snow. Generating the unique tree geometries of our teaser test scene and rendering them to the G-buffer takes 3.13 ms on an AMD Radeon RX 7900 XTX.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.143

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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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