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Paper Citation Record · LEDGER

VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2401.09047.

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

pith.paper-citation-record.v1
2401.09047 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:34:01.989009Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T20:42:37.758214Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 61943750-4d7f-4672-8ecd-a3f379070f94 · inbound

VideoPhy: Evaluating Physical Commonsense for Video Generation cites this paper.

VideoPhy: Evaluating Physical Commonsense for Video Generation VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 21

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arxiv_id, observed 2026-05-20T11:34:37.674070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation dd7cb2c8-e7b9-431a-9de9-bea657ea087a · inbound

Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement cites this paper.

Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 5

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arxiv_id, observed 2026-05-23T08:25:29.399875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation cda3b44a-09aa-412d-9c2f-51313623c117 · inbound

Se\~norita-2M: A High-Quality Instruction-based Dataset for General Video Editing by Video Specialists cites this paper.

Se\~norita-2M: A High-Quality Instruction-based Dataset for General Video Editing by Video Specialists VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 2023

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no resolver link, observed 2026-08-08T14:34:01.989009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8705ad1b-fe0f-4a58-b78f-2c4d74e7b2da · inbound

LayerFlow: A Unified Model for Layer-aware Video Generation cites this paper.

LayerFlow: A Unified Model for Layer-aware Video Generation VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 5

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no resolver link, observed 2026-08-07T10:50:50.064772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:50:50.064772Z digest=sha256:9ba561cecb37651353b604e655be5be08f7295bd69bee5bd9e0abce64efaae55

Observation 5097c0bc-7d99-47ec-9c4f-7c63f369dd8f · inbound

GenWorld: Towards Detecting AI-generated Real-world Simulation Videos cites this paper.

GenWorld: Towards Detecting AI-generated Real-world Simulation Videos VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 12

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no resolver link, observed 2026-08-07T04:17:41.966289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:17:41.966289Z digest=sha256:56d9fa34d88a910983db246b02622b9095a7791ab35d235ac31b189857722991

Observation 3a6eb67c-b15f-4451-9c3a-28473985f7ab · inbound

BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos cites this paper.

BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 4

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no resolver link, observed 2026-08-06T23:00:27.146720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:00:27.146720Z digest=sha256:acef0bcbfabef220ab62f745c2abea1112cf1ba5527207f3634cc155fc64251d

Observation 46fbe378-bbea-4889-a070-d4940cd91b6b · inbound

VMoBA: Mixture-of-Block Attention for Video Diffusion Models cites this paper.

VMoBA: Mixture-of-Block Attention for Video Diffusion Models VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 4

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no resolver link, observed 2026-08-06T21:34:02.735411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:34:02.735411Z digest=sha256:98ddedf732c756a7d8bccfc03e6ec559a732b55b6b405d81e85a590c0518b077

Observation d6e9ba4c-cd3e-4dda-b3c1-fca0e1b8492f · inbound

MedGen: Unlocking Medical Video Generation by Scaling Granularly-annotated Medical Videos cites this paper.

MedGen: Unlocking Medical Video Generation by Scaling Granularly-annotated Medical Videos VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 7

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no resolver link, observed 2026-08-06T19:25:08.486175Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:25:08.486175Z digest=sha256:12d73565f17da3931b5385bc314bc376051d48366d569217605fafc0bc6d3733

Observation f4f2ae74-7812-47bc-a722-ce191102550d · inbound

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling cites this paper.

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 14

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verified exact
arxiv_id, observed 2026-05-19T05:17:06.589884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-19T05:13:28.767788Z digest=sha256:c50cf2807ba2ba3db620f69a88cda48d0cae678e0b1bedc6a6033c8ea01abf71

Observation 8810f6f2-aaaf-4388-928d-e5c72e04051e · inbound

Look Beyond: Two-Stage Scene View Generation via Panorama and Video Diffusion cites this paper.

Look Beyond: Two-Stage Scene View Generation via Panorama and Video Diffusion VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 6

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no resolver link, observed 2026-08-05T13:15:10.831723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:15:10.831723Z digest=sha256:1bdddf988a0c1c7186842c429f5824779c5560bf9ae87a287256c0e6546a62b2

Observation a1bacb86-aa2e-41d6-aca7-f49386097804 · inbound

Enhancing Physical Plausibility in Video Generation by Reasoning the Implausibility cites this paper.

Enhancing Physical Plausibility in Video Generation by Reasoning the Implausibility VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 6

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verified exact
arxiv_id, observed 2026-05-18T12:56:24.379659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation ac82ad09-cc7d-4dd3-baa3-98f9088a33c8 · inbound

GT-SVJ: Generative-Transformer-Based Self-Supervised Video Judge For Efficient Video Reward Modeling cites this paper.

GT-SVJ: Generative-Transformer-Based Self-Supervised Video Judge For Efficient Video Reward Modeling VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 4

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verified exact
arxiv_id, observed 2026-05-25T07:16:41.958687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation fbc1b97f-5ed5-4c15-b885-f37a6d80aceb · inbound

MoRight: Motion Control Done Right cites this paper.

MoRight: Motion Control Done Right VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 12

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arxiv_id, observed 2026-05-11T06:26:01.094129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 003847c4-5857-45ac-8687-d5072e528f9c · inbound

DVAR: Adversarial Multi-Agent Debate for Video Authenticity Detection cites this paper.

DVAR: Adversarial Multi-Agent Debate for Video Authenticity Detection VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 8

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arxiv_id, observed 2026-05-10T07:52:13.606676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T07:51:04.580617Z digest=sha256:a9e2eea5437dfcda33a30ec6ea322b682bc030761ca2562016f1359e50b657dc

Observation 41c64761-3b6b-4330-9bf9-988ed60ec2d6 · inbound

TS-Attn: Temporal-wise Separable Attention for Multi-Event Video Generation cites this paper.

TS-Attn: Temporal-wise Separable Attention for Multi-Event Video Generation VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 3

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verified exact
arxiv_id, observed 2026-05-10T02:53:29.915192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T02:45:10.577070Z digest=sha256:d02c211b64345b360de184c331ea94ca1cc82d1f44d6214ae756833c1de45074

Observation f47ea10a-e547-4bec-9c29-f0a84e4105df · inbound

3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases cites this paper.

3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 194

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arxiv_id, observed 2026-05-12T10:26:30.499865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 631bb4b8-b465-4382-96a1-593c2ff13f5e · inbound

Substantial, Decomposable, and Invisible: Visual Context Misalignment in Instructional Videos for Physical Tasks cites this paper.

Substantial, Decomposable, and Invisible: Visual Context Misalignment in Instructional Videos for Physical Tasks VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 4

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arxiv_id, observed 2026-05-20T14:13:21.277981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation aa4dba0c-0520-4ab7-9817-6dea9b083c72 · inbound

Activation Concentration: Characterizing Column-Level Output Sparsity Across Diffusion Model Architectures cites this paper.

Activation Concentration: Characterizing Column-Level Output Sparsity Across Diffusion Model Architectures VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 4

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arxiv_id, observed 2026-06-28T20:42:37.759728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 693973c7-37fe-447c-abe1-2a7cd9eabb6e · inbound

Moving Alphabet: A Controlled Study of Training Data for Text-to-Video Generation cites this paper.

Moving Alphabet: A Controlled Study of Training Data for Text-to-Video Generation VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 3

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Unavailable: canonical work link unavailable.

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Observation 3b135105-3c35-475d-8696-05d0f6bf9543 · inbound

VIPER: Visual In-Context Physics Reasoning for Physically Plausible Video Generation cites this paper.

VIPER: Visual In-Context Physics Reasoning for Physically Plausible Video Generation VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 5

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no resolver link, observed 2026-07-30T21:29:21.953818Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T21:29:21.953818Z digest=sha256:1a2f2310f3815270e32f62db66ff0f8bbefe65e3e3d9d1d5b14d40245a62e862

Observation 91d7b5e8-339c-4c31-8f7d-b0d819ead46b · inbound

Retrieval-Driven Training-Free AI-Generated Video Attribution cites this paper.

Retrieval-Driven Training-Free AI-Generated Video Attribution VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 4

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no resolver link, observed 2026-08-03T16:36:46.182549Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T16:36:46.182549Z digest=sha256:e08d961154398f728f5d34cd99ed8d6a6e23d0cb09bc3e130115eacdff2b3772

Observation b4bc5000-6fc6-444d-b6d5-7d02b8d7ed83 · inbound

RAID: Towards Robust AI-Generated Image Detection with Bit-Reversed Images cites this paper.

RAID: Towards Robust AI-Generated Image Detection with Bit-Reversed Images VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 5

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no resolver link, observed 2026-08-03T16:19:59.276986Z

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Unavailable: canonical work link unavailable.

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