{"id":"W4390872824","doi":"10.1109/iccv51070.2023.01328","title":"SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulation","year":2023,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Encoding (memory); Representation (politics); Subspace topology; Embedding; Diffusion; Process (computing); Artificial intelligence; Algorithm; Theoretical computer science; 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":[0.0007216746,0.001070705,0.000837621,0.0007684244,0.0003864576,0.001097988,0.002300692,0.001567788,0.007106841],"category_scores_gemma":[0.001945274,0.0008339297,0.001772315,0.0006971144,0.0008322369,0.001520233,0.001753355,0.002234569,0.003088011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008761695,"about_ca_system_score_gemma":0.0008204522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004762013,"about_ca_topic_score_gemma":0.007715174,"domain_scores_codex":[0.9996032,0.00005696196,0.00001529661,0.000137164,0.0001512223,0.00003620726],"domain_scores_gemma":[0.9994735,0.0001912041,0.0000473736,0.0001673158,0.00006850477,0.00005200537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002263379,0.0001392011,0.001057609,0.0002442785,0.0001151341,0.0002001595,0.0001820881,0.601326,0.04133772,0.0313934,0.01262193,0.311156],"study_design_scores_gemma":[0.000009285181,0.0000185966,0.00007747224,0.000005258756,0.000005911619,0.00005931852,0.000004635272,0.9893028,0.003682194,0.004163071,0.002659999,0.00001138018],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003951476,0.0001852166,0.9906552,0.0001168424,0.00005603081,0.00004362685,0.0002324045,0.003830648,0.0009285487],"genre_scores_gemma":[0.2671514,0.0006545609,0.7140329,0.0003770399,0.00009024258,0.0002994841,0.002211348,0.002210009,0.01297297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007106841,"threshold_uncertainty_score":0.02377474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09473014830279598,"score_gpt":0.2507016288121131,"score_spread":0.1559714805093171,"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."}}