{"id":"W4409368447","doi":"10.1609/aaai.v39i4.32420","title":"PixelMan: Consistent Object Editing with Diffusion Models via Pixel Manipulation and Generation","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Huawei Technologies (Canada)","funders":"","keywords":"Object (grammar); Computer science; Diffusion; Pixel; Image editing; Computer graphics (images); Artificial intelligence; Computer vision; Physics; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001719449,0.0001602871,0.0001876065,0.0001417905,0.0001648017,0.000102913,0.0001699313,0.00006607764,0.000009640534],"category_scores_gemma":[0.00003847857,0.0001183996,0.00004921175,0.0003112819,0.00008341957,0.0001668084,0.00005109018,0.0001577727,0.000003185421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003646918,"about_ca_system_score_gemma":0.00001694351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003115424,"about_ca_topic_score_gemma":0.00004370094,"domain_scores_codex":[0.9990632,0.000006153638,0.0003274377,0.000234159,0.0002094418,0.0001596182],"domain_scores_gemma":[0.9994692,0.00002298817,0.00009338099,0.000102517,0.000278399,0.00003349558],"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":[0.00007843781,0.0001093993,0.001078732,0.0003071257,0.0001326125,3.670857e-7,0.001664554,0.3031157,0.3947248,0.1886914,0.00009458457,0.1100024],"study_design_scores_gemma":[0.00002125345,0.00003118144,0.00004979049,0.0002154942,0.00004694681,0.000001106439,0.0003198362,0.8722755,0.1109423,0.01598655,0.000004672274,0.0001053722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7001668,0.00004966775,0.2929404,0.0004262342,0.000164977,0.0001995255,0.000001977326,0.00009164607,0.00595873],"genre_scores_gemma":[0.9988682,0.00007923885,0.0008160531,0.0000422751,0.00006758036,0.00001589017,0.000001939839,0.00001207867,0.00009672986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5691599,"threshold_uncertainty_score":0.4828195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05856749182720886,"score_gpt":0.2501098546228132,"score_spread":0.1915423627956043,"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."}}