Paper Citation Record · LEDGER

Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2309.15818.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2309.15818 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:17:24.888752Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-17T15:07:39.832649Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation da46a16a-343c-46e5-934c-826c4a7a0248 · inbound

VideoPoet: A Large Language Model for Zero-Shot Video Generation cites this paper.

VideoPoet: A Large Language Model for Zero-Shot Video Generation Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-15T17:51:05.628714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a2eefc06-abbe-4521-bd74-678f58431d37 · inbound

OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation cites this paper.

OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:34:53.242156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c8a26450-2e36-4c1b-a2a1-dc8ee2314689 · inbound

CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer cites this paper.

CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 110

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:26:22.362066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 23c178f8-e3c1-4e2d-a33d-713b0800ab21 · inbound

Emu3: Next-Token Prediction is All You Need cites this paper.

Emu3: Next-Token Prediction is All You Need Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 102

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:56:09.503525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 31424b6a-0733-4bc8-b814-63af7358e723 · inbound

Autoregressive Video Generation without Vector Quantization cites this paper.

Autoregressive Video Generation without Vector Quantization Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-17T15:07:39.836921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 501f2b6f-5abd-4022-a7bc-d9cacc3cced1 · inbound

Separate Motion from Appearance: Customizing Motion via Customizing Text-to-Video Diffusion Models cites this paper.

Separate Motion from Appearance: Customizing Motion via Customizing Text-to-Video Diffusion Models Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T11:17:24.888752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dc15b45-3499-4c62-8900-cadfb86ff712 · inbound

Harness Local Rewards for Global Benefits: Effective Text-to-Video Generation Alignment with Patch-level Reward Models cites this paper.

Harness Local Rewards for Global Benefits: Effective Text-to-Video Generation Alignment with Patch-level Reward Models Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-09T11:26:17.427689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb6db24d-d8ec-4485-bcdd-6fcca71cb2a3 · inbound

Enhance-A-Video: Better Generated Video for Free cites this paper.

Enhance-A-Video: Better Generated Video for Free Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-08T12:32:46.501520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79daf691-163a-4b8d-a35a-e57b70bf965e · inbound

VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness cites this paper.

VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-14T18:42:03.022934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 10f83b87-7cdb-434c-b36f-e1beda082db1 · inbound

Show-o2: Improved Native Unified Multimodal Models cites this paper.

Show-o2: Improved Native Unified Multimodal Models Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 140

Resolution: verified exact; arxiv_id, observed 2026-05-12T18:51:15.937297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 11b1ca19-bb6c-4ff8-b3bf-9e08a21dc877 · inbound

AIGVE-MACS: Unified Multi-Aspect Commenting and Scoring Model for AI-Generated Video Evaluation cites this paper.

AIGVE-MACS: Unified Multi-Aspect Commenting and Scoring Model for AI-Generated Video Evaluation Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T21:03:03.827609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edc4c914-eb9e-4cdc-a452-ca4cdb0daf9f · inbound

NeoBabel: A Multilingual Open Tower for Visual Generation cites this paper.

NeoBabel: A Multilingual Open Tower for Visual Generation Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T19:15:30.153376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8307ad76-5cfa-48f0-92c3-b7aed07c9196 · inbound

Elevating 3D Models: High-Quality Texture and Geometry Refinement from a Low-Quality Model cites this paper.

Elevating 3D Models: High-Quality Texture and Geometry Refinement from a Low-Quality Model Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T17:14:41.760354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93bbbced-40a3-4aa5-852d-047e6952a905 · inbound

Accelerating Training of Autoregressive Video Generation Models via Local Optimization with Representation Continuity cites this paper.

Accelerating Training of Autoregressive Video Generation Models via Local Optimization with Representation Continuity Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T00:25:52.252058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.