{"id":"W4402952315","doi":"10.1007/978-3-031-72649-1_12","title":"MagDiff: Multi-alignment Diffusion for High-Fidelity Video Generation and Editing","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Fidelity; Computer graphics (images); Diffusion; Multimedia; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001038149,0.0003858612,0.0004170848,0.0005464221,0.0003524676,0.001029546,0.0009102246,0.0002400463,0.000006188446],"category_scores_gemma":[0.00009464585,0.0003328537,0.0001117359,0.0003405392,0.0001997686,0.0005090111,0.0009633066,0.0003198337,0.000008781438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002377606,"about_ca_system_score_gemma":0.0001346373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004074199,"about_ca_topic_score_gemma":0.0002279762,"domain_scores_codex":[0.9967909,0.00002470113,0.0005709296,0.001544952,0.0006746842,0.0003938567],"domain_scores_gemma":[0.9985156,0.0002088076,0.0002230331,0.0006983096,0.0002327426,0.0001214952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003282227,0.00004260967,0.00009426891,0.0001364047,0.00002905808,0.0000267586,0.0007893127,0.02220087,0.004258306,0.1062883,0.0001169748,0.8660138],"study_design_scores_gemma":[0.0002091232,0.00007577723,0.00008802727,0.0001900644,0.00002280201,0.000008776355,1.839638e-7,0.9101341,0.001368807,0.08690973,0.000661545,0.0003310404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003457929,0.0005966731,0.9949785,0.001255888,0.002154438,0.0004470653,0.00001282424,0.0001030167,0.0001057782],"genre_scores_gemma":[0.3015787,0.0001515139,0.6937551,0.001432303,0.002004858,0.00003641807,0.0000735385,0.00004454644,0.0009230475],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8879333,"threshold_uncertainty_score":0.9999123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124485895239514,"score_gpt":0.2514212562565962,"score_spread":0.230176397304201,"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."}}