{"id":"W4402716105","doi":"10.1109/cvpr52733.2024.00764","title":"4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology; University of Toronto","funders":"Army Research Office; Natural Sciences and Engineering Research Council of Canada; Simon Fraser University; National Science Foundation","keywords":"Distillation; Computer science; Sampling (signal processing); Natural language processing; Artificial intelligence; Telecommunications; Chemistry; Chromatography","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002477792,0.00009822951,0.0001024979,0.0002047762,0.0002586658,0.001617298,0.0001809441,0.00002741321,0.00004997337],"category_scores_gemma":[0.00003225892,0.00008457369,0.000063935,0.0006685781,0.00001079372,0.0006419902,0.00009524609,0.00005264968,0.00004731588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007745707,"about_ca_system_score_gemma":0.00008211275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008580429,"about_ca_topic_score_gemma":0.0000562321,"domain_scores_codex":[0.9989653,0.00002670386,0.0002308797,0.0003898764,0.0002389798,0.0001482599],"domain_scores_gemma":[0.9995362,0.00002618346,0.00003033421,0.0002619066,0.00007952753,0.00006591406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000220148,0.00002781312,0.00331433,0.00003791888,0.00005880543,0.00001407733,0.0005891401,0.3637594,0.04293797,0.3661211,0.001556687,0.2215806],"study_design_scores_gemma":[0.00002640417,0.00001127466,0.000506775,0.00002457361,0.00001370421,0.000004951336,0.000005479891,0.9938828,0.002685888,0.001243452,0.001475538,0.0001191779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1002319,0.00009168807,0.898143,0.000535751,0.0004231504,0.00008523335,0.000001776228,0.0001833499,0.0003041503],"genre_scores_gemma":[0.9005052,0.000004086146,0.09871551,0.0002456901,0.0003005586,0.000002706282,0.00003429708,0.000008426866,0.0001834593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8002734,"threshold_uncertainty_score":0.9994191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08414648768217287,"score_gpt":0.3050737473820094,"score_spread":0.2209272596998365,"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."}}