{"id":"W51279807","doi":"","title":"FACIAL ANIMATION WITH MOTION CAPTURE BASED ON SURFACE BLENDING","year":2017,"lang":"en","type":"article","venue":"International Conference on Computer Graphics Theory and Applications","topic":"Face recognition and analysis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Facial motion capture; Computer vision; Computer science; Artificial intelligence; Motion capture; Computer facial animation; Animation; Facial expression; Feature (linguistics); Computer animation; Focus (optics); Face (sociological concept); Surface (topology); Expression (computer science); Point (geometry); Computer graphics (images); Motion (physics); Point cloud; Facial recognition system; Feature extraction; Mathematics; Face detection; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002577313,0.0004208072,0.0003983936,0.0006713357,0.0002289455,0.0004248512,0.0005742413,0.0003629279,0.00184518],"category_scores_gemma":[0.0006562058,0.0003422704,0.0005315327,0.0004427225,0.0004089703,0.0005633316,0.0007281975,0.0004710504,0.0003662048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002503419,"about_ca_system_score_gemma":0.0001922314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001031631,"about_ca_topic_score_gemma":0.0009490277,"domain_scores_codex":[0.9997934,0.00002763877,0.000008253109,0.00004159673,0.000113878,0.0000154115],"domain_scores_gemma":[0.9998618,0.00004487439,0.00001514949,0.00004398562,0.00002349489,0.00001072488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002049183,0.00007623834,0.00130095,0.0001143745,0.0000590881,0.0001665119,0.0003712088,0.1950153,0.4425064,0.01590964,0.0009010344,0.3433744],"study_design_scores_gemma":[0.00002105474,0.000117917,0.001009714,0.00001466464,0.00002162862,0.000251618,0.00004427695,0.909372,0.0786801,0.00418514,0.006249321,0.00003258417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02557749,0.00006973381,0.9721051,0.00003365199,0.00001795876,0.00004025573,0.00003140473,0.000441254,0.001683121],"genre_scores_gemma":[0.3764264,0.0002650765,0.6194003,0.00004255747,0.00002553988,0.0001029473,0.0002136365,0.0002334045,0.003290174],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00184518,"threshold_uncertainty_score":0.006172717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02862089099017041,"score_gpt":0.2823401209317764,"score_spread":0.253719229941606,"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."}}