{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:MBHWRE6KFLTH77OQWUPZHSWHR6","short_pith_number":"pith:MBHWRE6K","schema_version":"1.0","canonical_sha256":"604f6893ca2ae67ffdd0b51f93cac78f95a72031f416338bcb1dea668639afb4","source":{"kind":"arxiv","id":"2103.05916","version":2},"attestation_state":"computed","paper":{"title":"SocialInteractionGAN: Multi-person Interaction Sequence Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.NE","authors_text":"Dominique Vaufreydaz (M-PSI), Louis Airale (M-PSI, PERCEPTION), Xavier Alameda-Pineda (PERCEPTION)","submitted_at":"2021-03-10T08:11:34Z","abstract_excerpt":"Prediction of human actions in social interactions has important applications in the design of social robots or artificial avatars. In this paper, we focus on a unimodal representation of interactions and propose to tackle interaction generation in a data-driven fashion. In particular, we model human interaction generation as a discrete multi-sequence generation problem and present SocialInteractionGAN, a novel adversarial architecture for conditional interaction generation. Our model builds on a recurrent encoder-decoder generator network and a dual-stream discriminator, that jointly evaluate"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2103.05916","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2021-03-10T08:11:34Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c3f7abfb46f8947b7f07a4418d485ec8018efb37158ddd6deac7e4fa0f1d4fb2","abstract_canon_sha256":"29838752394774a29700e8c8486ece3c889560999413046c9b1160e6b514b637"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:56:10.447881Z","signature_b64":"QHyBfILt4j7UJFvEECPcYyZr/E0ob8Gpl5me4eKQGZxm8Mq7EiOOMBqm2Anh6dkb8HmQ+KGUSBDW7Fdlppx5AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"604f6893ca2ae67ffdd0b51f93cac78f95a72031f416338bcb1dea668639afb4","last_reissued_at":"2026-07-05T04:56:10.447448Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:56:10.447448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SocialInteractionGAN: Multi-person Interaction Sequence Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.NE","authors_text":"Dominique Vaufreydaz (M-PSI), Louis Airale (M-PSI, PERCEPTION), Xavier Alameda-Pineda (PERCEPTION)","submitted_at":"2021-03-10T08:11:34Z","abstract_excerpt":"Prediction of human actions in social interactions has important applications in the design of social robots or artificial avatars. In this paper, we focus on a unimodal representation of interactions and propose to tackle interaction generation in a data-driven fashion. In particular, we model human interaction generation as a discrete multi-sequence generation problem and present SocialInteractionGAN, a novel adversarial architecture for conditional interaction generation. Our model builds on a recurrent encoder-decoder generator network and a dual-stream discriminator, that jointly evaluate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.05916","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2103.05916/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2103.05916","created_at":"2026-07-05T04:56:10.447505+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.05916v2","created_at":"2026-07-05T04:56:10.447505+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.05916","created_at":"2026-07-05T04:56:10.447505+00:00"},{"alias_kind":"pith_short_12","alias_value":"MBHWRE6KFLTH","created_at":"2026-07-05T04:56:10.447505+00:00"},{"alias_kind":"pith_short_16","alias_value":"MBHWRE6KFLTH77OQ","created_at":"2026-07-05T04:56:10.447505+00:00"},{"alias_kind":"pith_short_8","alias_value":"MBHWRE6K","created_at":"2026-07-05T04:56:10.447505+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.01915","citing_title":"Social Processes: Probabilistic Meta-learning for Adaptive Multiparty Interaction Forecasting","ref_index":84,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MBHWRE6KFLTH77OQWUPZHSWHR6","json":"https://pith.science/pith/MBHWRE6KFLTH77OQWUPZHSWHR6.json","graph_json":"https://pith.science/api/pith-number/MBHWRE6KFLTH77OQWUPZHSWHR6/graph.json","events_json":"https://pith.science/api/pith-number/MBHWRE6KFLTH77OQWUPZHSWHR6/events.json","paper":"https://pith.science/paper/MBHWRE6K"},"agent_actions":{"view_html":"https://pith.science/pith/MBHWRE6KFLTH77OQWUPZHSWHR6","download_json":"https://pith.science/pith/MBHWRE6KFLTH77OQWUPZHSWHR6.json","view_paper":"https://pith.science/paper/MBHWRE6K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.05916&json=true","fetch_graph":"https://pith.science/api/pith-number/MBHWRE6KFLTH77OQWUPZHSWHR6/graph.json","fetch_events":"https://pith.science/api/pith-number/MBHWRE6KFLTH77OQWUPZHSWHR6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MBHWRE6KFLTH77OQWUPZHSWHR6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MBHWRE6KFLTH77OQWUPZHSWHR6/action/storage_attestation","attest_author":"https://pith.science/pith/MBHWRE6KFLTH77OQWUPZHSWHR6/action/author_attestation","sign_citation":"https://pith.science/pith/MBHWRE6KFLTH77OQWUPZHSWHR6/action/citation_signature","submit_replication":"https://pith.science/pith/MBHWRE6KFLTH77OQWUPZHSWHR6/action/replication_record"}},"created_at":"2026-07-05T04:56:10.447505+00:00","updated_at":"2026-07-05T04:56:10.447505+00:00"}