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Paper Citation Record · LEDGER

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

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 36 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

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measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 36 of 36 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:46:46.342097Z

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

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Outbound references

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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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation d842ab78-c03a-4438-ae63-8b06167cf955 · inbound

VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models cites this paper.

VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 36

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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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 79df2fb7-f014-4d68-a8ea-539968660bab · inbound

Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metric cites this paper.

Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metric VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 11

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Observation 29075946-6ef8-4bf3-b349-d6634be62a44 · inbound

MoTrans: Customized Motion Transfer with Text-driven Video Diffusion Models cites this paper.

MoTrans: Customized Motion Transfer with Text-driven Video Diffusion Models VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 7

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Observation 6d9d3a85-22cd-4eab-ae5d-e96b73321327 · inbound

Seeing Beyond Views: Multi-View Driving Scene Video Generation with Holistic Attention cites this paper.

Seeing Beyond Views: Multi-View Driving Scene Video Generation with Holistic Attention VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 5

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source=pdf_text observed=2026-08-11T22:21:55.035366Z digest=sha256:22953733247d21197d2d7a1fc381a253a61950b981bbae0261eea632eda10325

Observation defca067-f689-450d-a551-8d22d4d28709 · inbound

Imagine360: Immersive 360 Video Generation from Perspective Anchor cites this paper.

Imagine360: Immersive 360 Video Generation from Perspective Anchor VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 5

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source=pdf_text observed=2026-08-11T22:23:15.411724Z digest=sha256:e1f224d666a89a5b9b5683a192779ce3bbf5cb63d7ec13e7cf41d4a94aea35e8

Observation 8e0ca905-fd28-49c0-bb89-10eb207807d1 · inbound

GenMAC: Compositional Text-to-Video Generation with Multi-Agent Collaboration cites this paper.

GenMAC: Compositional Text-to-Video Generation with Multi-Agent Collaboration VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 8

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source=pdf_text observed=2026-08-11T21:28:20.884384Z digest=sha256:d1386a5eac9652c89920caa2626ceaa8b258d94d340458ee7cfb8e20c77c7dd1

Observation ba12f3e2-9e05-44c5-9b51-004e8cca5fd4 · inbound

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training cites this paper.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 6

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Observation 762016c1-d55a-48b4-8c1d-e2c33c196619 · inbound

UniPaint: Unified Space-time Video Inpainting via Mixture-of-Experts cites this paper.

UniPaint: Unified Space-time Video Inpainting via Mixture-of-Experts VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 11

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Observation 04f13c57-e3a7-4a6a-977e-2af8701cdb6f · inbound

UFO: Enhancing Diffusion-Based Video Generation with a Uniform Frame Organizer cites this paper.

UFO: Enhancing Diffusion-Based Video Generation with a Uniform Frame Organizer VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 5

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source=arxiv_source observed=2026-08-11T17:10:40.999394Z digest=sha256:3fd755dd9bf2aa38f3096f056e742e0181f10dcb6cd273f7bf568a7eff0bcf06

Observation d075885d-723e-4978-82c4-923cef888a28 · inbound

Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models cites this paper.

Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 5

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source=arxiv_source observed=2026-08-11T18:35:56.184506Z digest=sha256:b20689b61fa99f6ab7e5eee8dd49ee17e108ee753c7331f12b9a0d4a8a36cb55

Observation 7dd7c79c-d318-43fa-941a-28b93962ab1d · inbound

TransPixeler: Advancing Text-to-Video Generation with Transparency cites this paper.

TransPixeler: Advancing Text-to-Video Generation with Transparency VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 8

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Observation 1999cf07-5763-4ba1-adf0-3cb1e28b087e · inbound

Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion cites this paper.

Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 5

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source=arxiv_source observed=2026-08-10T20:14:50.687451Z digest=sha256:22a43fa5a2f41f136502ab1e85058da8ebd5fa8ed7b21904dc8f3a8f6982f852

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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source=pdf_text observed=2026-08-08T14:34:01.989009Z digest=sha256:333748474418ace377264d1aafa315ec7013c6ea971b5dd37f7ee839167b492d

Observation 8cb38cda-cf41-44bc-b751-6bf9f3ae61a2 · inbound

LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation cites this paper.

LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 79

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source=pdf_text observed=2026-08-15T20:46:46.342097Z digest=sha256:024fcb3fc39393d90c6133e0576d5c1b31d1deb41a6b05bb42f8d6675b17594c

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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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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source=pdf_text observed=2026-08-05T13:15:10.831723Z digest=sha256:251eb3a39cd53882aa13bbfe0b1445145d1c5ee27f7d2e7316b8c15c8861bab5

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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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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arxiv_id, observed 2026-05-25T07:16:41.958687Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-20T14:10:09.223869Z digest=sha256:c2057cbad5d50098dae77618ee47f2a80e5c86ac9b5bfd36457c4a63aebe93a5

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

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+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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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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source=pdf_text observed=2026-07-30T21:29:21.953818Z digest=sha256:664bba904eceaa08a3c0cc9835d3bd1a089f7a34971b5c0609b1ede5a7e76bfc

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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source=pdf_text observed=2026-08-03T16:36:46.182549Z digest=sha256:ca852a031b2db00c03ea4c9edf0d2211d6ac391c41fd4c7acf33e24c3ca2240c

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T16:19:59.276986Z digest=sha256:18ba6299d23e62b6c2b1d2cf96da8d0d0ab2f964e9f16d06c473441baaeb0f2e

Observation 6629297a-c95b-46a9-aef5-f10d550a8b50 · inbound

GeoRoute: Geometry-Aware Hybrid Inference for Traffic Future-Frame Prediction cites this paper.

GeoRoute: Geometry-Aware Hybrid Inference for Traffic Future-Frame Prediction VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T16:27:38.687257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T16:27:38.687257Z digest=sha256:2036036f24ab2aa40fd083757b2880bbdafffa9daad357e24ec84f4bf9ceee99