{"id":"W4396512647","doi":"10.1111/cgf.15061","title":"Text‐to‐3D Shape Generation","year":2024,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Generative grammar; Rendering (computer graphics); Text generation; Representation (politics); Artificial intelligence; Generative model; Categorization; Data science; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008461285,0.0001210658,0.0001080458,0.0002881891,0.00006169446,0.000167223,0.0001172315,0.00006689315,0.00002512404],"category_scores_gemma":[0.000001619685,0.0001171999,0.0001059124,0.0004825517,0.000008848845,0.00008413459,0.00003572619,0.0001518062,0.0001780716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001622123,"about_ca_system_score_gemma":0.000009215563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003676984,"about_ca_topic_score_gemma":0.0000180268,"domain_scores_codex":[0.9993221,0.000007943576,0.0001463108,0.0001956056,0.0001169168,0.000211084],"domain_scores_gemma":[0.9997057,0.00001604853,0.00000448672,0.0001720072,0.00002632551,0.00007543914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[9.311215e-7,0.00001615242,0.0002646646,0.0001012852,0.0002422585,0.00002781038,0.000346912,0.6171778,0.001000491,0.01915406,0.08443805,0.2772296],"study_design_scores_gemma":[0.00002785927,0.00001625151,0.00003734129,0.00003247851,0.00001980293,0.000003322807,0.000002461302,0.9796101,0.00013759,0.0004346279,0.0195326,0.0001455789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04735936,0.001111446,0.9488258,0.0004244194,0.001105244,0.00005064265,0.000006516442,0.0007642221,0.0003523198],"genre_scores_gemma":[0.9937425,0.00009270991,0.005061414,0.0004374598,0.0005331534,0.000008602866,0.00002931089,0.00003461307,0.00006023565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9463831,"threshold_uncertainty_score":0.4779273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539807407071325,"score_gpt":0.2221319658426268,"score_spread":0.2067338917719135,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}