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Paper Citation Record · LEDGER

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly

As of 8 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2507.19924.

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

pith.paper-citation-record.v1
2507.19924 v2

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:57:04.663532Z

measured 62 of 62 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0915f7ee-f875-4ff9-b9cf-3f917bc2c231 · outbound

This paper cites https://vchitect.intern-ai.org.cn, 2024.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly https://vchitect.intern-ai.org.cn, 2024

Reference 1

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Observation 10179ea4-7f1d-4d91-bec3-c5f71a2f6b6a · outbound

This paper cites AI-generated video detection via spatial-temporal anomaly learning.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly AI-generated video detection via spatial-temporal anomaly learning

Reference 2

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Source-reported events for the cited work

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Observation 65254c27-10b5-49a2-9846-910733f694b1 · outbound

This paper cites Is space-time attention all you need for video understanding? InICML, page 4, 2021.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Is space-time attention all you need for video understanding? InICML, page 4, 2021

Reference 3

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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 5520d5e9-52c5-4aeb-ac23-c505e5722a34 · outbound

This paper cites Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2e64a08b-e971-46f7-8353-420bfabf7d71 · outbound

This paper cites Video generation models as world simulators.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Video generation models as world simulators

Reference 5

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source=pdf_text observed=2026-08-06T13:57:04.367406Z digest=sha256:5b3a8108aaa1e97b5ec3db62bafdb9e9e3c087307aee8d6dcf9e176fbf13a793

Observation 7f250038-88a4-4643-8ea2-14b9c1edab8f · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Emerg- ing properties in self-supervised vision transformers

Reference 6

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verified fuzzy
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Source-reported events for the cited work

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Observation 64bb309a-7cb6-4182-9dd9-658e6ad1a9ed · outbound

This paper cites What matters in detecting ai-generated videos like sora? arXiv preprint arXiv:2406.19568, 2024.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly What matters in detecting ai-generated videos like sora? arXiv preprint arXiv:2406.19568, 2024

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 58b24c88-9f7e-4bb5-aec2-36f3b8728ba1 · outbound

This paper cites Multi-view clustering via deep concept factorization.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Multi-view clustering via deep concept factorization

Reference 8

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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 0280e3ab-442d-4bc2-b5e5-35c36b402062 · outbound

This paper cites VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 845f37cf-0abe-4362-9e26-f0291c5ed0d5 · outbound

This paper cites https://research.runwayml.com/gen2, 2023.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly https://research.runwayml.com/gen2, 2023

Reference 10

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verified fuzzy
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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 6da0cb16-ba7a-4cb8-a9d9-c5f1cfa77306 · outbound

This paper cites https://runwayml.com/research/introducing- gen-3-alpha, 2024.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly https://runwayml.com/research/introducing- gen-3-alpha, 2024

Reference 11

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verified fuzzy
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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 70fe0df4-8f2f-41cf-aefd-1473eb84e09e · outbound

This paper cites Hierarchical fine-grained im- age forgery detection and localization.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Hierarchical fine-grained im- age forgery detection and localization

Reference 12

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verified fuzzy
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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 94936ad9-d07b-4b78-8bd8-73b6e9c6391c · outbound

This paper cites VEnhancer: Generative Space-Time Enhancement for Video Generation.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly VEnhancer: Generative Space-Time Enhancement for Video Generation

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 7b8c3aaa-a584-46d6-bb68-22380bfc196d · outbound

This paper cites Video dif- fusion models.Advances in Neural Information Processing Systems, 35:8633–8646, 2022.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Video dif- fusion models.Advances in Neural Information Processing Systems, 35:8633–8646, 2022

Reference 14

Resolution
verified fuzzy
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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 da56d66e-7c5c-49fd-baf6-3679cf1c64c4 · outbound

This paper cites Vbench: Comprehensive bench- mark suite for video generative models.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Vbench: Comprehensive bench- mark suite for video generative models

Reference 15

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verified fuzzy
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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 85f54177-4cf0-4f12-aa33-e0fa0fb4ffb3 · outbound

This paper cites Scaling Laws for Neural Language Models.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Scaling Laws for Neural Language Models

Reference 16

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Source-reported events for the cited work

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Observation 4d053b37-c262-4f75-ad1d-a5ce7925aa9e · outbound

This paper cites Large-scale video classification with convolutional neural networks.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Large-scale video classification with convolutional neural networks

Reference 17

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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 cfe64271-82bd-4516-be08-ccb7ba6da883 · outbound

This paper cites The Kinetics Human Action Video Dataset.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly The Kinetics Human Action Video Dataset

Reference 18

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Observation b02b280a-75ac-4065-8944-5eab1635068e · outbound

This paper cites https://klingai.kuaishou.com/, 2024.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly https://klingai.kuaishou.com/, 2024

Reference 19

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verified fuzzy
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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 f421f113-060f-4d27-b2a3-9fed35ee963c · outbound

This paper cites HunyuanVideo: A Systematic Framework For Large Video Generative Models.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly HunyuanVideo: A Systematic Framework For Large Video Generative Models

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8c46eacd-70eb-4ad5-8560-117c253831ab · outbound

This paper cites https://pika.art, 2024.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly https://pika.art, 2024

Reference 21

Resolution
verified fuzzy
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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=2026-08-06T13:57:04.455497Z digest=sha256:9bf9cf515a43cf2472beed6b482c7e036ba87f93f593784bacddaae77d72c7d8

Observation 7e64a9d5-23c8-487b-be20-3ef31ac4909a · outbound

This paper cites Learning blind video temporal consistency.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Learning blind video temporal consistency

Reference 22

Resolution
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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 c61b6694-e3d1-43af-a7b2-759fd2462bfb · outbound

This paper cites Blind video temporal consistency via deep video prior.Advances in Neu- ral Information Processing Systems, 33:1083–1093, 2020.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Blind video temporal consistency via deep video prior.Advances in Neu- ral Information Processing Systems, 33:1083–1093, 2020

Reference 23

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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 dd1b183c-25e4-49d0-8094-ec7142a15b1c · outbound

This paper cites A Comprehensive Survey on Human Video Generation: Challenges, Methods, and Insights.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly A Comprehensive Survey on Human Video Generation: Challenges, Methods, and Insights

Reference 24

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Observation f9dd9395-673c-4978-ae73-d7dcf938d604 · outbound

This paper cites Unmasked teacher: Towards training-efficient video foundation models.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Unmasked teacher: Towards training-efficient video foundation models

Reference 25

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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 8163d72d-4924-4546-803d-3539a645ca32 · outbound

This paper cites Evalcrafter: Benchmarking and eval- uating large video generation models.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Evalcrafter: Benchmarking and eval- uating large video generation models

Reference 26

Resolution
verified fuzzy
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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 0e04851a-2b60-4801-93f4-3ca2c757aab9 · outbound

This paper cites Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f47b92eb-aea3-42b5-b85d-bd2e01c2244f · outbound

This paper cites https://hailuoai.com/video, 2024.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly https://hailuoai.com/video, 2024

Reference 28

Resolution
verified fuzzy
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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 da026e46-ccdd-4afc-af0f-5b1396cef422 · outbound

This paper cites Towards uni- versal fake image detectors that generalize across genera- tive models.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Towards uni- versal fake image detectors that generalize across genera- tive models

Reference 29

Resolution
verified fuzzy
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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 821ae6ed-6fa6-44a3-8b01-a803ae878be2 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly DINOv2: Learning Robust Visual Features without Supervision

Reference 30

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:57:04.499775Z digest=sha256:d275317fb85f7a58c882fbd31526e5248afe13218d4bd2dde67e7381cb678440

Observation a9c22209-7f9e-4af3-8662-3a7373fd2ca7 · outbound

This paper cites Fine-grained bipar- tite concept factorization for clustering.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Fine-grained bipar- tite concept factorization for clustering

Reference 31

Resolution
verified fuzzy
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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 93fb96ba-6863-449a-86d3-2301dd1a20c4 · outbound

This paper cites Fatezero: Fus- ing attentions for zero-shot text-based video editing.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Fatezero: Fus- ing attentions for zero-shot text-based video editing

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.654428Z

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 fb886f94-cfd2-4bc8-9f3c-54366cba5604 · outbound

This paper cites Thinking in frequency: Face forgery detection by min- ing frequency-aware clues.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Thinking in frequency: Face forgery detection by min- ing frequency-aware clues

Reference 33

Resolution
verified fuzzy
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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 b1761874-bd64-4f16-ac63-fbdaafcbc9fb · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Learning transferable visual models from natural language supervi- sion

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.622912Z

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=2026-08-06T13:57:04.520890Z digest=sha256:4e6193e026943493b2532fb7db102d020ba0e474fae9945dddbe2a5db6437390

Observation f62dc0ff-c991-4cfa-84f2-0a928dc8319e · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly SAM 2: Segment Anything in Images and Videos

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T13:57:04.526479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:57:04.526479Z digest=sha256:fdf5c11aa01c4bfe06a67e815da11a7decaba270558641fe64edc3e33d9a4662

Observation 870faa30-2263-46f5-bbcd-a907997c7f59 · outbound

This paper cites De-fake: Detection and attribution of fake images generated by text- to-image generation models.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly De-fake: Detection and attribution of fake images generated by text- to-image generation models

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.607761Z

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=2026-08-06T13:57:04.532042Z digest=sha256:8521db210e63a9b075ba4e081bf0421508ce7fbcaa23f8ad15e1a827ce6266ea

Observation 9621853d-e808-4cbe-a1cf-788173b347ee · outbound

This paper cites RepVideo: Rethinking Cross-Layer Representation for Video Generation.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly RepVideo: Rethinking Cross-Layer Representation for Video Generation

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T13:57:04.536373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:57:04.536373Z digest=sha256:a181e48eca245239bf10e0b65bdab77ec9f7491bb3c887033ffaf6bcb94f284d

Observation 7b48b4a7-df27-410b-9331-5871ccd1d82b · outbound

This paper cites Two-stream con- volutional networks for action recognition in videos.Ad- vances in neural information processing systems, 27, 2014.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Two-stream con- volutional networks for action recognition in videos.Ad- vances in neural information processing systems, 27, 2014

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.573379Z

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=2026-08-06T13:57:04.541661Z digest=sha256:885e886757a7019839c4551e08ba034a005a74538a1c79ad7c1346d51754406d

Observation b3b02bf2-9bb3-4da6-8583-009b74dded78 · outbound

This paper cites Denoising Diffusion Implicit Models.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Denoising Diffusion Implicit Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T13:57:04.546640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:57:04.546640Z digest=sha256:2e91753545ade9c9213fcbd29df62c82ca6ec824909aea5054b8afbc7e2e6941

Observation ff065fc5-aca2-4433-a52b-9422068c549c · outbound

This paper cites On learn- ing multi-modal forgery representation for diffusion gener- ated video detection.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly On learn- ing multi-modal forgery representation for diffusion gener- ated video detection

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.537567Z

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=2026-08-06T13:57:04.553144Z digest=sha256:41f071548d5fba93c58a92426b7084fc0d8047dc276203decac31198ed8a8d52

Observation ddf49aac-e658-4285-8aff-279af00150c0 · outbound

This paper cites Rethinking the up-sampling op- erations in cnn-based generative network for generalizable deepfake detection.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Rethinking the up-sampling op- erations in cnn-based generative network for generalizable deepfake detection

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.492029Z

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=2026-08-06T13:57:04.557530Z digest=sha256:76b37fef4a7c85e89f9db8b3d909b0591b030a43c7da9872b51965322ab35027

Observation 13941333-9d54-41fb-9b57-feb3f5f9e977 · outbound

This paper cites Raft: Recurrent all-pairs field transforms for optical flow.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Raft: Recurrent all-pairs field transforms for optical flow

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T13:57:04.561628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:57:04.561628Z digest=sha256:c1e39526ea8f0c0a0c9aabaf76fb352879c6d467ba88767626ed56b2fc19c7ef

Observation 4be8cc68-da9a-43cd-8a3e-857f7ec6dd84 · outbound

This paper cites Learning spatiotemporal features with 3d convolutional networks.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Learning spatiotemporal features with 3d convolutional networks

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T13:57:04.567292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:57:04.567292Z digest=sha256:462a0a92d868bab2923458ad2328d8654000edd04aa9f3a10a134c7393dfbc94

Observation 287fcf92-4dd3-43b5-9924-488cf36576a2 · outbound

This paper cites https://wanxai.com/, 2025.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly https://wanxai.com/, 2025

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.433073Z

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=2026-08-06T13:57:04.573144Z digest=sha256:1757bde568228a8d92863d2960de8b77ba9d4638c51a298776f09a15f2a36502

Observation a54887a3-c13d-4434-864a-a3629b8b54ae · outbound

This paper cites ModelScope Text-to-Video Technical Report.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly ModelScope Text-to-Video Technical Report

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T13:57:04.577514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:57:04.577514Z digest=sha256:de87b2f1f7fdf729c561c34225bb2bf953585740dd3c717e996570efb7a72990

Observation 576d992d-34f0-421b-9082-911fa33c8632 · outbound

This paper cites Cnn-generated images are surprisingly easy to spot.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Cnn-generated images are surprisingly easy to spot

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.414401Z

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=2026-08-06T13:57:04.582427Z digest=sha256:0b38c6eecec44e872e9de5bcc34543cb4fb68eba94e12dec42955c0b80c43288

Observation 49664a06-07a5-49c3-a551-aeaea6f87f3f · outbound

This paper cites InternVideo2: Scaling Foundation Models for Multimodal Video Understanding.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly InternVideo2: Scaling Foundation Models for Multimodal Video Understanding

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T13:57:04.588143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:57:04.588143Z digest=sha256:b0a8800f3d8104afea45639044e77d2cae59588dcb4e7c0e59b5a57c57f2d824

Observation 0e70c6ca-39ac-40ef-883d-ea6abd2d7e81 · outbound

This paper cites Dire for diffusion-generated image detection.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Dire for diffusion-generated image detection

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.398528Z

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=2026-08-06T13:57:04.592938Z digest=sha256:9aec6488cc18df179bceb2ac05990e0ef2b37e38f5836b44193b2238890e1eb3

Observation 090fe47b-0c5a-4758-9f2f-e54c96f3eafa · outbound

This paper cites Finepose: Fine- grained prompt-driven 3d human pose estimation via diffu- sion models.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Finepose: Fine- grained prompt-driven 3d human pose estimation via diffu- sion models

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.384765Z

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=2026-08-06T13:57:04.599107Z digest=sha256:210037c012538adfcb30557273d9cb6821742270d4626a15fd2c43e974c2b139

Observation 19256ad0-f45e-466f-93aa-56d91f0e5c10 · outbound

This paper cites Fineparser: A fine-grained spatio-temporal action parser for human-centric action quality assessment.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Fineparser: A fine-grained spatio-temporal action parser for human-centric action quality assessment

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.370577Z

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=2026-08-06T13:57:04.603929Z digest=sha256:c9eef71355899c74006cdec4f13372046cde0a44393738d825c020eeca107858

Observation 363e232b-631c-48df-87f7-df057363f3e8 · outbound

This paper cites Human motion video genera- tion: A survey.Authorea Preprints, 2024.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Human motion video genera- tion: A survey.Authorea Preprints, 2024

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.356199Z

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=2026-08-06T13:57:04.609892Z digest=sha256:35f2492919ec819173c01269132b3be2cd417be1cad60a9c9ae2ee27e8cef267

Observation 13aa1db2-34f6-40dc-a205-a5a4b6fae393 · outbound

This paper cites Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T13:57:04.614646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:57:04.614646Z digest=sha256:da49db3839f56620157debe1238979e165b2e1f1f93465f2d6b6dd101c93509f

Observation b09e4455-06f3-4c0d-8aed-e113330ba988 · outbound

This paper cites Depth Anything V2.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Depth Anything V2

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-06T13:57:04.619264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:57:04.619264Z digest=sha256:89eae37caccf2948b4b9abe0c22ed44daf72ade75159f990f4f4fc95eb272840

Observation f9c086cc-12cb-4a91-b418-29ca0d344268 · outbound

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

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-06T13:57:04.624361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:57:04.624361Z digest=sha256:95aef889bbb36a701e7f787b930dc9a958005dd91586a42adfc996f7240499d0

Observation d10aca95-b507-47c4-a89f-567b6b43f0d8 · outbound

This paper cites Stedge: Self-training edge detection with multilayer teaching and regularization.IEEE Transactions on Neural Networks and Learning Systems, 2023.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Stedge: Self-training edge detection with multilayer teaching and regularization.IEEE Transactions on Neural Networks and Learning Systems, 2023

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.340129Z

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=2026-08-06T13:57:04.629202Z digest=sha256:8e3a0de56f282a157aa8655d57a2310958d0c8865c8e55c67cfd819dc3762ace

Observation 17d2de94-5e86-40ab-8500-016ddbd3d6cf · outbound

This paper cites Diffusionedge: Diffusion probabilistic model for crisp edge detection.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Diffusionedge: Diffusion probabilistic model for crisp edge detection

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.324381Z

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=2026-08-06T13:57:04.633796Z digest=sha256:6f0c02f404a2b379ca2f046e44539d42f9ab1ddb8bf1f62fedbe8889bdbd50ee

Observation 0765e47d-901f-431b-8299-33569df632ae · outbound

This paper cites an unresolved cited work.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Unresolved cited work

Reference 57

Resolution
unresolved
raw_fallback, observed 2026-08-06T13:57:05.308841Z

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=2026-08-06T13:57:04.638325Z digest=sha256:76dcca2d350ec3b0d9910bf7cba57252a1da47433cf4df5323f251c241ccd2ab

Observation 5c6c2acd-9100-4167-bd06-7298ef145230 · outbound

This paper cites Identity- preserving text-to-video generation by frequency decompo- sition.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Identity- preserving text-to-video generation by frequency decompo- sition

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.292534Z

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=2026-08-06T13:57:04.642650Z digest=sha256:07c7d1779aa7025742077d25692af3e8af4be6addfc0153f5dff6567a2e5049d

Observation 8519dfa5-1cfa-4702-ae7a-e0fc6614a987 · outbound

This paper cites Multi- scale video anomaly detection by multi-grained spatio- temporal representation learning.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Multi- scale video anomaly detection by multi-grained spatio- temporal representation learning

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.275307Z

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=2026-08-06T13:57:04.648656Z digest=sha256:6ec489ba90a506f3f6cf8b07d809cbcd06f63303aae572ef9bf8b28f84d976cb

Observation e922e64d-fe81-480c-8a33-011f79f2673c · outbound

This paper cites Pose-guided transformer for fine-grained action quality assessment.IEEE Transactions on Circuits and Systems for Video Technology, pages 1–1,.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Pose-guided transformer for fine-grained action quality assessment.IEEE Transactions on Circuits and Systems for Video Technology, pages 1–1,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:57:05.259296Z

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=2026-08-06T13:57:04.654218Z digest=sha256:240985bcd0b4b511c5a90e149c420193e6a6937cd39fa8f15105df73e8bc9c68

Observation 095c8183-e70b-4959-a272-00087b909712 · outbound

This paper cites an unresolved cited work.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Unresolved cited work

Reference 62

Resolution
unresolved
raw_fallback, observed 2026-08-06T13:57:05.226405Z

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=2026-08-06T13:57:04.663532Z digest=sha256:1269542b1d3b1595f6bc0ebc03ccbf39ff20274710100f563237b68696ec2085

Observation 0ec6af70-5789-49ae-8773-854f54d3f961 · outbound

This paper cites an unresolved cited work.

HumanSAM: Classifying Human-centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly Unresolved cited work

Reference 2025

Resolution
unresolved
raw_fallback, observed 2026-08-06T13:57:05.242128Z

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=2026-08-06T13:57:04.658633Z digest=sha256:d1351cfeaa4cd25a010ed8bfa387d49298327d1044e91c01a3d208eb263f1623

Pith citing papers

No inbound Pith citation observations are available.