{"id":"W4417125414","doi":"10.1145/3757377.3763889","title":"MVP4D: Multi-View Portrait Video Diffusion for Animatable 4D Avatars","year":2025,"lang":"","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Avatar; Rendering (computer graphics); Viewpoints; Process (computing); Virtual reality; Virtual image; Consistency (knowledge bases); Augmented reality","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001022806,0.000627095,0.0008484295,0.0002470293,0.001242564,0.001255028,0.001530022,0.0002570108,0.0008572899],"category_scores_gemma":[0.0003199451,0.0005580994,0.000484627,0.00114126,0.0002233997,0.001272544,0.001059816,0.0002606006,0.00008963013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001185716,"about_ca_system_score_gemma":0.0006209826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004780639,"about_ca_topic_score_gemma":0.000339075,"domain_scores_codex":[0.9957042,0.0002158741,0.001044091,0.001553095,0.0003632576,0.001119518],"domain_scores_gemma":[0.9972187,0.0004540743,0.0002794942,0.001294734,0.0004894448,0.0002635471],"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":[0.000132146,0.00124668,0.0004246179,0.0005730482,0.0003906125,0.00002086454,0.0005205976,0.004853586,0.01066169,0.06869012,0.1184537,0.7940323],"study_design_scores_gemma":[0.001596553,0.0001729408,0.0008523092,0.0003509644,0.0001261366,0.000003104115,0.00007659059,0.7776242,0.009829826,0.001152448,0.2076651,0.0005497878],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003563668,0.004909094,0.9802346,0.004298679,0.003050548,0.001579382,0.00005424962,0.0001398968,0.005377246],"genre_scores_gemma":[0.2448667,0.001506841,0.6809721,0.005439754,0.0004924746,0.0001924607,0.00003581442,0.00005385844,0.06644001],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7934825,"threshold_uncertainty_score":0.9997818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02415886262790738,"score_gpt":0.2795018970375476,"score_spread":0.2553430344096402,"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."}}