{"id":"W4410777883","doi":"10.1111/cgf.70052","title":"ASMR: Adaptive Skeleton‐Mesh Rigging and Skinning via 2D Generative Prior","year":2025,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Human Motion and Animation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Skinning; Computer science; Skeleton (computer programming); Generative grammar; Computer graphics (images); Mesh generation; Artificial intelligence; Computer vision; Engineering drawing; Engineering; Finite element method; Mechanical engineering; Programming language","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":[0.001531703,0.00135129,0.001362014,0.001656909,0.000457802,0.001138191,0.002441003,0.002080902,0.004685481],"category_scores_gemma":[0.003425708,0.001029574,0.00184391,0.0009534846,0.001432127,0.001142852,0.001900642,0.002409856,0.001980079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008357482,"about_ca_system_score_gemma":0.0009515638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0066309,"about_ca_topic_score_gemma":0.01153021,"domain_scores_codex":[0.9990494,0.000215497,0.00003844155,0.0003100731,0.0003080086,0.00007857737],"domain_scores_gemma":[0.9984965,0.0007074213,0.0001297445,0.0004110914,0.0001707438,0.00008454851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000117337,0.00009618395,0.0009793492,0.0001413547,0.00008655511,0.000127618,0.0001442644,0.7596112,0.01824906,0.007935029,0.003623435,0.2088887],"study_design_scores_gemma":[0.000004345881,0.00001375991,0.00008995969,0.000008238951,0.000003604814,0.00002870728,0.000004558703,0.9960206,0.001707041,0.001432485,0.0006796455,0.000007075911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007273546,0.0001526396,0.9883268,0.00008009295,0.00003548836,0.00005798421,0.00007869646,0.002933101,0.001061612],"genre_scores_gemma":[0.3171492,0.000301014,0.6733997,0.0004369772,0.00008539833,0.0002341492,0.0008858178,0.00197989,0.005527727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0066309,"threshold_uncertainty_score":0.01567453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008271803109512746,"score_gpt":0.2154326429917004,"score_spread":0.2071608398821876,"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."}}