{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006765102,0.001434092,0.0007266313,0.001284865,0.0004780887,0.001562577,0.002114732,0.00143723,0.02668001],"category_scores_gemma":[0.004345271,0.0005479245,0.001638747,0.0009715558,0.0008124874,0.001336241,0.002517799,0.001142151,0.01163475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005082096,"about_ca_system_score_gemma":0.0005096607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00164566,"about_ca_topic_score_gemma":0.002325601,"domain_scores_codex":[0.9993017,0.0001029611,0.0000382214,0.0001899653,0.0003194698,0.00004760392],"domain_scores_gemma":[0.9981081,0.000677112,0.00008430809,0.0007015622,0.0003500551,0.0000787438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004337665,0.0001808218,0.001166711,0.000559545,0.00008692256,0.0005691479,0.0003248944,0.09356204,0.04362275,0.01483251,0.05659067,0.7880702],"study_design_scores_gemma":[0.00008109587,0.0001350017,0.00083661,0.00008572475,0.00003160659,0.0005004891,0.0001238227,0.8763206,0.05168541,0.02751155,0.04261562,0.00007257161],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01454888,0.0005854563,0.9380782,0.0004853253,0.0005805948,0.0003737737,0.002108942,0.02882629,0.01441249],"genre_scores_gemma":[0.3269271,0.0008767105,0.6350284,0.0007965271,0.0002428492,0.0005011658,0.01088531,0.005362716,0.01937933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02668001,"threshold_uncertainty_score":0.08925354,"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."}}