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Paper Citation Record · LEDGER

Photorealistic Video Generation with Diffusion Models

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 45 inbound Pith citation observations for arXiv:2312.06662.

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

pith.paper-citation-record.v1
2312.06662 v1

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measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 45 of 45 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:17:38.685991Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T23:59:06.270972Z

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f3ac6d4a-7d3f-4712-a79a-fcfab2ff46af · inbound

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

VideoPoet: A Large Language Model for Zero-Shot Video Generation Photorealistic Video Generation with Diffusion Models

Reference 16

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arxiv_id, observed 2026-05-15T17:51:05.696684Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-15T17:51:05.465548Z digest=sha256:9cecd3b22eb23e209eca8a2246e866bc49ad3794942a1a35c7964764f68dd355

Observation a30b442c-f944-48d1-ac3c-51399a50592e · inbound

Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models cites this paper.

Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models Photorealistic Video Generation with Diffusion Models

Reference 65

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arxiv_id, observed 2026-05-13T13:43:11.133262Z

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source=pdf_text observed=2026-05-13T13:43:11.024069Z digest=sha256:0efc0239bf08d2df49ec9e763429c2ec1ec53d1c1e6f7d78532a74ff9e325b0f

Observation 83ba4456-c92e-4173-ad7c-b949f4c528b3 · inbound

CameraCtrl: Enabling Camera Control for Text-to-Video Generation cites this paper.

CameraCtrl: Enabling Camera Control for Text-to-Video Generation Photorealistic Video Generation with Diffusion Models

Reference 114

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arxiv_id, observed 2026-05-13T02:06:23.609147Z

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source=arxiv_source observed=2026-05-13T02:06:23.410241Z digest=sha256:89aa2f4c5d62f31821f959dbb9e7b0cae633fc66af5a519f0bfdd41022d1bd44

Observation c9feceff-95e8-4b3e-baec-0e8f184a96fa · inbound

Diffusion Models Are Real-Time Game Engines cites this paper.

Diffusion Models Are Real-Time Game Engines Photorealistic Video Generation with Diffusion Models

Reference 64

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arxiv_id, observed 2026-05-16T12:04:44.253409Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-16T12:04:44.162343Z digest=sha256:eef270e17806d358123044eb38b11e2a9fcfffa16844932d8e1892a2123d959b

Observation fcfa539f-3a60-4c06-91f7-08487f1a35db · inbound

Movie Gen: A Cast of Media Foundation Models cites this paper.

Movie Gen: A Cast of Media Foundation Models Photorealistic Video Generation with Diffusion Models

Reference 22

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arxiv_id, observed 2026-05-11T14:16:23.388206Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-11T14:16:18.521699Z digest=sha256:6240e9befa3ff6cfef088a7795217300deae46b72843718bfa983833b871fddf

Observation 326c7df2-d902-4793-8d7f-c32d2556a744 · inbound

LaVin-DiT: Large Vision Diffusion Transformer cites this paper.

LaVin-DiT: Large Vision Diffusion Transformer Photorealistic Video Generation with Diffusion Models

Reference 26

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source=pdf_text observed=2026-08-12T18:31:28.824916Z digest=sha256:caf2053eac505fcc96c3e2fb6460fc7cb6378427b817eb7bbe5c73b82c4ff376

Observation b7da27d1-0969-47ed-afbc-874079721592 · inbound

SpatialDreamer: Self-supervised Stereo Video Synthesis from Monocular Input cites this paper.

SpatialDreamer: Self-supervised Stereo Video Synthesis from Monocular Input Photorealistic Video Generation with Diffusion Models

Reference 20

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source=pdf_text observed=2026-08-12T18:23:28.619614Z digest=sha256:f15b01757779ab713cddce851df82bd2ebb1d96d5e4b4bd481e64c9ad985d29f

Observation db49d1d6-96b5-4d72-afeb-27d3c7b383ed · inbound

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

VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models Photorealistic Video Generation with Diffusion Models

Reference 31

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source=pdf_text observed=2026-08-12T16:22:43.885081Z digest=sha256:4e65ccdcc9df7e6cf04020f313d194c7a4c90828e98b98fe21f7275047f02c74

Observation 51ab998c-1849-4323-9129-c10b6e684341 · inbound

Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack cites this paper.

Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack Photorealistic Video Generation with Diffusion Models

Reference 7

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source=pdf_text observed=2026-08-12T13:10:06.163293Z digest=sha256:8cfb40a1f2f948a03d529e9eaa02b874deb1c946eea7ac707accab0750e30636

Observation 9924372b-4880-403a-9df9-878a71f22ef2 · inbound

I2VControl: Disentangled and Unified Video Motion Synthesis Control cites this paper.

I2VControl: Disentangled and Unified Video Motion Synthesis Control Photorealistic Video Generation with Diffusion Models

Reference 13

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source=pdf_text observed=2026-08-12T12:36:49.227114Z digest=sha256:4e003748149ddafad7c09a9e7c1cf775abcc10118dffe2eb6698865c1b7fc399

Observation 3c72ac3f-05d3-4f0f-b1a3-86efe4801777 · inbound

ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration cites this paper.

ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration Photorealistic Video Generation with Diffusion Models

Reference 16

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source=pdf_text observed=2026-08-12T10:09:30.560157Z digest=sha256:589f22b7e6d1b7ac3149894fb9448258a42f9750a625b1db40c7b56ee4bf0976

Observation 04fd7fa5-41df-4b4f-aaf1-7f95a8f68394 · inbound

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

MoTrans: Customized Motion Transfer with Text-driven Video Diffusion Models Photorealistic Video Generation with Diffusion Models

Reference 17

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source=pdf_text observed=2026-08-12T04:31:13.391642Z digest=sha256:3bd5da40812bba9ac35396bc78cc7eef8a5df37120c66007fbd1c03450cbd9e4

Observation 087efbe0-b9a4-4ab9-9432-f7c3f38a3c80 · inbound

ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions cites this paper.

ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions Photorealistic Video Generation with Diffusion Models

Reference 24

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source=pdf_text observed=2026-08-12T00:04:08.772500Z digest=sha256:66a9942a5e2571a8a7e0caba1914e1a458b301bfd4121750ad311bdb19527e69

Observation b124ba76-0c48-474f-b54b-9c607d9288c6 · inbound

ARCON: Advancing Auto-Regressive Continuation for Driving Videos cites this paper.

ARCON: Advancing Auto-Regressive Continuation for Driving Videos Photorealistic Video Generation with Diffusion Models

Reference 20

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source=pdf_text observed=2026-08-11T22:11:52.484101Z digest=sha256:ef03af928e2c8dfd481b6a12c6abc93f3ff6e4587aa98961f9b81a66e43de7b7

Observation 3c32eeff-7f4b-40b8-a49e-6c7b23399fe4 · inbound

Mind the Time: Temporally-Controlled Multi-Event Video Generation cites this paper.

Mind the Time: Temporally-Controlled Multi-Event Video Generation Photorealistic Video Generation with Diffusion Models

Reference 29

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source=pdf_text observed=2026-08-11T20:55:01.943350Z digest=sha256:a1368fda7a19a37d53b1e54761fc8b70e5137e9656f192f58657a4a19c40c3eb

Observation a839529d-cd95-4a27-933c-3499cfb4054e · inbound

AniSora: Exploring the Frontiers of Animation Video Generation in the Sora Era cites this paper.

AniSora: Exploring the Frontiers of Animation Video Generation in the Sora Era Photorealistic Video Generation with Diffusion Models

Reference 2014

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source=pdf_text observed=2026-08-11T16:14:30.561174Z digest=sha256:8aef646e177f98ce21527ee63eecd4de5ddae32976047d1fdeae26d6e8a1193c

Observation 052fda56-3df9-419b-ab7d-135594bdc82d · inbound

SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device cites this paper.

SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device Photorealistic Video Generation with Diffusion Models

Reference 15

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source=pdf_text observed=2026-08-11T15:58:35.481731Z digest=sha256:3e0d6026e7af07ec95fa7be5c20f996a8730c81a9f057a5419e8489216edf55b

Observation de6e8369-924f-4aa2-86ac-d29c617eb4d0 · inbound

Generative Physical AI in Vision: A Survey cites this paper.

Generative Physical AI in Vision: A Survey Photorealistic Video Generation with Diffusion Models

Reference 30

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source=pdf_text observed=2026-08-10T18:53:00.040443Z digest=sha256:1ba00d70ddc40736228500421d9b9c78a1cc5724538662a76e216868765938a9

Observation 68405ef8-bd5e-47c7-b576-04b8bf3ba5a9 · inbound

Image Motion Blur Removal in the Temporal Dimension with Video Diffusion Models cites this paper.

Image Motion Blur Removal in the Temporal Dimension with Video Diffusion Models Photorealistic Video Generation with Diffusion Models

Reference 19

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source=pdf_text observed=2026-08-10T17:04:25.668990Z digest=sha256:b9a085ddb783d2b2a9f738d7fb7d0f37dd245ac718a46c26fbfbcaa5d9ae6027

Observation 024ecec7-7c5b-4682-a07f-d037cc366ca0 · inbound

CascadeV: An Implementation of Wurstchen Architecture for Video Generation cites this paper.

CascadeV: An Implementation of Wurstchen Architecture for Video Generation Photorealistic Video Generation with Diffusion Models

Reference 11

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source=pdf_text observed=2026-08-10T12:00:07.531922Z digest=sha256:70e19e5cbc655036126755344cec145b57f1dc87b3bc96ee654dcb61f9c8abd2

Observation d986b921-42e1-41b7-86bb-34b5f13f1df7 · inbound

OmniHuman-1: Rethinking the Scaling-Up of One-Stage Conditioned Human Animation Models cites this paper.

OmniHuman-1: Rethinking the Scaling-Up of One-Stage Conditioned Human Animation Models Photorealistic Video Generation with Diffusion Models

Reference 18

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source=pdf_text observed=2026-08-09T16:47:12.944417Z digest=sha256:e1bd95c5ea1e3a08314aa1ab50244a078dde1b8eadae0ae07b06174421ea1bca

Observation 084190aa-6b2b-4847-94df-d69ed9eff100 · inbound

Elucidating the Preconditioning in Consistency Distillation cites this paper.

Elucidating the Preconditioning in Consistency Distillation Photorealistic Video Generation with Diffusion Models

Reference 2014

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source=pdf_text observed=2026-08-09T10:42:54.372159Z digest=sha256:6da2fe5bdccfdd79b184d5cb1f65ec4acc6921c705cf388357f17d9ddcaafb14

Observation 6f4eda9c-22d6-40d1-9867-623cc893613e · inbound

DriVerse: Navigation World Model for Driving Simulation via Multimodal Trajectory Prompting and Motion Alignment cites this paper.

DriVerse: Navigation World Model for Driving Simulation via Multimodal Trajectory Prompting and Motion Alignment Photorealistic Video Generation with Diffusion Models

Reference 21

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arxiv_id, observed 2026-05-22T17:51:54.847724Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-22T17:50:59.797593Z digest=sha256:96343d3be1e6a3f9eff16ac1eec6fc780926d4368b221f0cf23ec25dfbcecdab

Observation 5f047eb8-026d-4dd3-b7d0-09eb4260100f · inbound

ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Models cites this paper.

ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Models Photorealistic Video Generation with Diffusion Models

Reference 15

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source=pdf_text observed=2026-08-15T22:17:38.685991Z digest=sha256:f3f9d4a9aab9329a759f015de60e9a55086c3ba6b5013b6c16bc1cc1cbf13016

Observation c99fa51a-c5a3-4a82-9e15-f7aa895e90d3 · inbound

Generating time-consistent dynamics with discriminator-guided image diffusion models cites this paper.

Generating time-consistent dynamics with discriminator-guided image diffusion models Photorealistic Video Generation with Diffusion Models

Reference 18

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source=pdf_text observed=2026-08-15T21:45:39.014788Z digest=sha256:f527bf1c777f0ff2a273d9ef525bc11a2808bba1c32af1051c9c4c4ab5429fc6

Observation e18d67e2-6821-4e60-8bc7-f1f4ce907d13 · inbound

COSMIC: Enabling Full-Stack Co-Design and Optimization of Distributed Machine Learning Systems cites this paper.

COSMIC: Enabling Full-Stack Co-Design and Optimization of Distributed Machine Learning Systems Photorealistic Video Generation with Diffusion Models

Reference 13

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source=pdf_text observed=2026-08-07T15:31:08.409975Z digest=sha256:4e0ff037ca25edc169df9d303b2a115e23164222b0f91df0c8d05ac23a49a759

Observation 61bf796b-3e09-4c2e-87b7-cc8273bb1b61 · inbound

Interspatial Attention for Efficient 4D Human Video Generation cites this paper.

Interspatial Attention for Efficient 4D Human Video Generation Photorealistic Video Generation with Diffusion Models

Reference 2023

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source=pdf_text observed=2026-08-07T15:14:20.876919Z digest=sha256:e53c3cdfd8d0f1974fa9d7246dcf12decb0307f45f961ceb3c715f6b5e8447a7

Observation b77c0f73-d6df-4241-b1fe-047f7242ff90 · inbound

HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters cites this paper.

HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters Photorealistic Video Generation with Diffusion Models

Reference 10

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source=pdf_text observed=2026-08-07T14:02:27.393281Z digest=sha256:b027c3d587bc80f0d1a14f066d1b7027291baf690f46e0d0cbd5d59d8d69ef0a

Observation 06ee8144-68cc-4d5f-a7fb-b04e5c81214d · inbound

MotionPro: A Precise Motion Controller for Image-to-Video Generation cites this paper.

MotionPro: A Precise Motion Controller for Image-to-Video Generation Photorealistic Video Generation with Diffusion Models

Reference 18

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source=pdf_text observed=2026-08-07T14:01:04.203514Z digest=sha256:4dd2663c2eeda08f883650dbf26666fea7e2b4b9c7356c3688e9dfb5af0d9aed

Observation de3c8e32-8ac4-447b-9930-3376b4452548 · inbound

OmniV2V: Versatile Video Generation and Editing via Dynamic Content Manipulation cites this paper.

OmniV2V: Versatile Video Generation and Editing via Dynamic Content Manipulation Photorealistic Video Generation with Diffusion Models

Reference 15

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source=pdf_text observed=2026-08-07T11:39:47.248333Z digest=sha256:7dca5f42a83a4be3c9efaa444d95a509e384f2689a1ec491af62ab627e31fed0

Observation 6f3eef75-6580-4523-89c6-db09473afdc4 · inbound

Exploring Diffusion Transformer Designs via Grafting cites this paper.

Exploring Diffusion Transformer Designs via Grafting Photorealistic Video Generation with Diffusion Models

Reference 3

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source=pdf_text observed=2026-08-07T10:28:59.043553Z digest=sha256:070759dd8eb58e57a100f112a9c48540815269a188f4902aa903636c15a36eed

Observation 9aa97890-913d-46a5-b73b-1e8b60084817 · inbound

ContentV: Efficient Training of Video Generation Models with Limited Compute cites this paper.

ContentV: Efficient Training of Video Generation Models with Limited Compute Photorealistic Video Generation with Diffusion Models

Reference 15

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source=pdf_text observed=2026-08-07T10:28:30.991346Z digest=sha256:25065bcdebb653221f6cd85eb6a7a03190ac56deb1ca2fcee86989ce6b406070

Observation 164efe7c-5622-4255-b10c-c1789a45d6c6 · inbound

Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low CFG Scales cites this paper.

Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low CFG Scales Photorealistic Video Generation with Diffusion Models

Reference 14

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source=pdf_text observed=2026-08-15T18:31:43.570995Z digest=sha256:a75729165dc29c57d44e54a593e653af94f275656230c7e4a3d7bda1b0554a60

Observation 5296e79a-5b8e-4535-bcda-9fd25decd065 · inbound

HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling cites this paper.

HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling Photorealistic Video Generation with Diffusion Models

Reference 2024

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source=pdf_text observed=2026-08-06T22:54:20.474769Z digest=sha256:5276edea6c9fb461a9440c984aa3aac2da999faa194579968014e44769326f38

Observation 1909a206-97fe-472a-ac8f-167bc2ca06ff · inbound

Populate-A-Scene: Affordance-Aware Human Video Generation cites this paper.

Populate-A-Scene: Affordance-Aware Human Video Generation Photorealistic Video Generation with Diffusion Models

Reference 2011

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source=pdf_text observed=2026-08-06T21:23:12.701371Z digest=sha256:a20d4c5076b1ff48a43aa2cf4f01883296eabeadd0bdace127c80bf5ef60f694

Observation df264099-6cb3-4fa3-84af-63348cd90316 · inbound

MambaVideo for Discrete Video Tokenization with Channel-Split Quantization cites this paper.

MambaVideo for Discrete Video Tokenization with Channel-Split Quantization Photorealistic Video Generation with Diffusion Models

Reference 9

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source=pdf_text observed=2026-08-06T19:52:28.048781Z digest=sha256:31c4de38f7f48b65909d09eb4c5dd2ab36fe78d3e6e97add5df68c09274aae75

Observation cfb24bf3-a349-4e53-8ef7-8937b27eefbc · inbound

Leveraging Pre-Trained Visual Models for AI-Generated Video Detection cites this paper.

Leveraging Pre-Trained Visual Models for AI-Generated Video Detection Photorealistic Video Generation with Diffusion Models

Reference 8

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T16:31:51.608613Z digest=sha256:cb7962ec1301453d378116859d357d4632557223ec7d6313d973fee52f08c744

Observation e494224f-783c-44d9-a4e8-553a716c5c68 · inbound

Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments cites this paper.

Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments Photorealistic Video Generation with Diffusion Models

Reference 18

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unresolved
no resolver link, observed 2026-08-03T13:00:53.564961Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T13:00:53.564961Z digest=sha256:bea0d39f5363666d484e23cb40a4ed0293825deebd024fdf3e4ef6957a23f0a9

Observation 9aacd0c8-bc0e-49ab-8c5f-6d1807527a11 · inbound

VIGOR: VIdeo Geometry-Oriented Reward for Temporal Generative Alignment cites this paper.

VIGOR: VIdeo Geometry-Oriented Reward for Temporal Generative Alignment Photorealistic Video Generation with Diffusion Models

Reference 15

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unresolved
no resolver link, observed 2026-07-13T23:54:26.281624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T23:54:26.281624Z digest=sha256:11b729b0d28033665546bb830b3fb438c371498bd76fb8e0123dc434ac00b274

Observation 3e850b7c-8641-40ec-8a7b-5fbd5956c6e7 · inbound

ActionParty: Multi-Subject Action Binding in Generative Video Games cites this paper.

ActionParty: Multi-Subject Action Binding in Generative Video Games Photorealistic Video Generation with Diffusion Models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-03T00:56:26.681009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T00:56:26.681009Z digest=sha256:234f137d5aac98238a55ff862e4fe4f7efb1f004600a18cd14ac32378e4f0dc3

Observation 46724067-61c0-43c5-8aa0-489cb5a9c90a · inbound

CaloArt: Large-Patch x-Prediction Diffusion Transformers for High-Granularity Calorimeter Shower Generation cites this paper.

CaloArt: Large-Patch x-Prediction Diffusion Transformers for High-Granularity Calorimeter Shower Generation Photorealistic Video Generation with Diffusion Models

Reference 67

Resolution
verified exact
arxiv_id, observed 2026-05-13T04:42:15.910426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-13T04:40:25.725622Z digest=sha256:dc7d83c458272a474664730c19e944cc84fa5a0e034425f4140d324acb32e2c7

Observation fea15afe-2dd3-40df-a52e-8c915ee709b1 · inbound

Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment cites this paper.

Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment Photorealistic Video Generation with Diffusion Models

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-21T06:24:41.374599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-21T06:24:04.891249Z digest=sha256:8608f781b663e32be13e9aeaa9b8ebb46301920db98cd81a965ee0555dd6ff0a

Observation c478c0f3-14a3-4589-b0f0-73b3a17e2a9f · inbound

GF-DiT: Scheduling Parallelism for Diffusion Transformer Serving cites this paper.

GF-DiT: Scheduling Parallelism for Diffusion Transformer Serving Photorealistic Video Generation with Diffusion Models

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-03T16:28:39.252659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-06-27T05:35:01.339479Z digest=sha256:c2e5c5dae81c1ea83a465a4d2301bedc4685ed6ad5072028d6e4a9a8f45585fe

Observation 0223f2b8-b3e5-4bca-b223-61ee4a7cb261 · inbound

GF-DiT: Scheduling Parallelism for Diffusion Transformer Serving cites this paper.

GF-DiT: Scheduling Parallelism for Diffusion Transformer Serving Photorealistic Video Generation with Diffusion Models

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-03T23:59:06.273126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-07-03T23:50:39.241880Z digest=sha256:d9a4e81cb6bbff5f4a13f328f8f5558e8705318b06147ac448219558ae931c6c

Observation 6c3ff821-3318-4749-98d1-c52e4123b75a · inbound

WaiT for the Signal: Simple Frequency-Aware Flow-Matching cites this paper.

WaiT for the Signal: Simple Frequency-Aware Flow-Matching Photorealistic Video Generation with Diffusion Models

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-03T00:34:35.423426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T00:34:35.423426Z digest=sha256:237de00077ec1764acb2d2b60597c34dfff38c166513702d78bc335fdd8e21c0