{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:T2NZJFS4MYBV3BIDHQ5UVMMPT6","short_pith_number":"pith:T2NZJFS4","schema_version":"1.0","canonical_sha256":"9e9b94965c66035d85033c3b4ab18f9fa49c61ffcb04d1316ad07c59e5f75ac1","source":{"kind":"arxiv","id":"2406.19680","version":2},"attestation_state":"computed","paper":{"title":"MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.CV","authors_text":"Fangyuan Zou, Han Wang, Jiaxi Gu, Junqi Cheng, Li-Wen Wang, Yuang Zhang, Yuefeng Zhu","submitted_at":"2024-06-28T06:40:53Z","abstract_excerpt":"In recent years, generative artificial intelligence has achieved significant advancements in the field of image generation, spawning a variety of applications. However, video generation still faces considerable challenges in various aspects, such as controllability, video length, and richness of details, which hinder the application and popularization of this technology. In this work, we propose a controllable video generation framework, dubbed MimicMotion, which can generate high-quality videos of arbitrary length mimicking specific motion guidance. Compared with previous methods, our approac"},"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":"2406.19680","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-28T06:40:53Z","cross_cats_sorted":["cs.AI","cs.MM"],"title_canon_sha256":"ba2b27b11fbd3d2bb49b69f826d2f9aff0b9273d1f302115d12abb7fd0e33acd","abstract_canon_sha256":"531295f3091ea509f6aa6b6cc30a7c0c0e7d0fd91ff9658fed284b54bf6d01cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:28:10.457448Z","signature_b64":"5yqEP7p/6nmhGl7+K3X5tz6qRAtaiVhO/f2PFEcMtkc3J4Eo47DQPEk8eAsQxMuwwJ8wI6yDcUGIWQ/KzN12AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e9b94965c66035d85033c3b4ab18f9fa49c61ffcb04d1316ad07c59e5f75ac1","last_reissued_at":"2026-07-05T11:28:10.456930Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:28:10.456930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.CV","authors_text":"Fangyuan Zou, Han Wang, Jiaxi Gu, Junqi Cheng, Li-Wen Wang, Yuang Zhang, Yuefeng Zhu","submitted_at":"2024-06-28T06:40:53Z","abstract_excerpt":"In recent years, generative artificial intelligence has achieved significant advancements in the field of image generation, spawning a variety of applications. However, video generation still faces considerable challenges in various aspects, such as controllability, video length, and richness of details, which hinder the application and popularization of this technology. In this work, we propose a controllable video generation framework, dubbed MimicMotion, which can generate high-quality videos of arbitrary length mimicking specific motion guidance. Compared with previous methods, our approac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.19680","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/2406.19680/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":"2406.19680","created_at":"2026-07-05T11:28:10.456994+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.19680v2","created_at":"2026-07-05T11:28:10.456994+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.19680","created_at":"2026-07-05T11:28:10.456994+00:00"},{"alias_kind":"pith_short_12","alias_value":"T2NZJFS4MYBV","created_at":"2026-07-05T11:28:10.456994+00:00"},{"alias_kind":"pith_short_16","alias_value":"T2NZJFS4MYBV3BID","created_at":"2026-07-05T11:28:10.456994+00:00"},{"alias_kind":"pith_short_8","alias_value":"T2NZJFS4","created_at":"2026-07-05T11:28:10.456994+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":21,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20233","citing_title":"Cinematic Compositing Using Character-Environment-Harmonized Video Generation Models","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10804","citing_title":"SCAIL-2: Unifying Controlled Character Animation with End-to-end In-Context Conditioning","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30514","citing_title":"3D Scene-Adaptive Trajectory-Controllable Human Image Animation with Camera Movement","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06903","citing_title":"Beyond Skeletons: Learning Animation Directly from Driving Videos with Same2X Training Strategy","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06885","citing_title":"FreeAnimate: Training-Free Human Image Animation with Preview-Guided Denoising","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.02000","citing_title":"Towards 3D-Aware Video Diffusion Models: Render-Free Human Motion Control with Mesh Tokenization","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15042","citing_title":"EverAnimate: Minute-Scale Human Animation via Latent Flow Restoration","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31981","citing_title":"LUNA: Learning Universal 3D Human Animation Beyond Skinning","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30514","citing_title":"3D Scene-Adaptive Trajectory-Controllable Human Image Animation with Camera Movement","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28026","citing_title":"EMOSH: Expressive Motion and Shape Disentanglement for Human Animation","ref_index":86,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27964","citing_title":"Directing the World: Fast Autoregressive Video Generation with Compositional Human-Camera Control","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2511.22940","citing_title":"One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer","ref_index":62,"is_internal_anchor":false},{"citing_arxiv_id":"2511.19320","citing_title":"SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2601.10632","citing_title":"CoMoVi: Co-Generation of 3D Human Motions and Realistic Videos","ref_index":112,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03305","citing_title":"HVG-3D: Bridging Real and Simulation Domains for 3D-Conditional Hand-Object Interaction Video Synthesis","ref_index":82,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03637","citing_title":"Bridging the Embodiment Gap: Disentangled Cross-Embodiment Video Editing","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06010","citing_title":"Adding Thermal Awareness to Visual Systems in Real-Time via Distilled Diffusion Models","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19720","citing_title":"ReImagine: Rethinking Controllable High-Quality Human Video Generation via Image-First Synthesis","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01720","citing_title":"SignVerse-2M: A Two-Million-Clip Pose-Native Universe of 55+ Sign Languages","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05961","citing_title":"HumANDiff: Articulated Noise Diffusion for Motion-Consistent Human Video Generation","ref_index":82,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21291","citing_title":"Exploring the Role of Synthetic Data Augmentation in Controllable Human-Centric Video Generation","ref_index":48,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T2NZJFS4MYBV3BIDHQ5UVMMPT6","json":"https://pith.science/pith/T2NZJFS4MYBV3BIDHQ5UVMMPT6.json","graph_json":"https://pith.science/api/pith-number/T2NZJFS4MYBV3BIDHQ5UVMMPT6/graph.json","events_json":"https://pith.science/api/pith-number/T2NZJFS4MYBV3BIDHQ5UVMMPT6/events.json","paper":"https://pith.science/paper/T2NZJFS4"},"agent_actions":{"view_html":"https://pith.science/pith/T2NZJFS4MYBV3BIDHQ5UVMMPT6","download_json":"https://pith.science/pith/T2NZJFS4MYBV3BIDHQ5UVMMPT6.json","view_paper":"https://pith.science/paper/T2NZJFS4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.19680&json=true","fetch_graph":"https://pith.science/api/pith-number/T2NZJFS4MYBV3BIDHQ5UVMMPT6/graph.json","fetch_events":"https://pith.science/api/pith-number/T2NZJFS4MYBV3BIDHQ5UVMMPT6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T2NZJFS4MYBV3BIDHQ5UVMMPT6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T2NZJFS4MYBV3BIDHQ5UVMMPT6/action/storage_attestation","attest_author":"https://pith.science/pith/T2NZJFS4MYBV3BIDHQ5UVMMPT6/action/author_attestation","sign_citation":"https://pith.science/pith/T2NZJFS4MYBV3BIDHQ5UVMMPT6/action/citation_signature","submit_replication":"https://pith.science/pith/T2NZJFS4MYBV3BIDHQ5UVMMPT6/action/replication_record"}},"created_at":"2026-07-05T11:28:10.456994+00:00","updated_at":"2026-07-05T11:28:10.456994+00:00"}