Paper Citation Record · LEDGER

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts

As of 12 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2509.08818.

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

pith.paper-citation-record.v1
2509.08818 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T20:09:21.378850Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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Outbound references

Observation 3c493b3e-6a00-4edb-83e4-62c705d55c30 · outbound

This paper cites an unresolved cited work.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T20:09:21.135490Z

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Observation 370b322c-0cb2-4cf0-8b68-f0bb4275d4a8 · outbound

This paper cites Accessed: July 10, 2025.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Accessed: July 10, 2025

Reference 2

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Observation f86710b1-2295-4d6d-8d77-847727824d29 · outbound

This paper cites Video generation models as world simulators.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Video generation models as world simulators

Reference 3

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Observation 4ff34cb7-291f-43d9-b515-4d0211610f9c · outbound

This paper cites SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Model.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Model

Reference 4

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Observation 9092d513-ceff-4f82-8dfc-c438a00cccfe · outbound

This paper cites Watching the big artifacts: Exposing deepfake videos via bi-granularity artifacts.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Watching the big artifacts: Exposing deepfake videos via bi-granularity artifacts

Reference 5

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Observation 1444219e-9c9e-4045-a12d-5b376ff29c11 · outbound

This paper cites Videocrafter2: Overcoming data limitations for high-quality video diffusion models, 2024.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Videocrafter2: Overcoming data limitations for high-quality video diffusion models, 2024

Reference 6

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Observation 5b7c49e1-c945-48a1-afc8-21c68a04f639 · outbound

This paper cites Super-Samples from Kernel Herding.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Super-Samples from Kernel Herding

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T20:09:21.168815Z

Source-reported events for the cited work

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Observation da8c8c03-9898-455d-9573-1c67003dd8ce · outbound

This paper cites Exploring the Naturalness of AI-Generated Images.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Exploring the Naturalness of AI-Generated Images

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T20:09:21.175216Z

Source-reported events for the cited work

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Observation 2f6db746-41e6-47e4-91f7-8652292ee014 · outbound

This paper cites Gaia: Rethinking action quality assessment for ai-generated videos.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Gaia: Rethinking action quality assessment for ai-generated videos

Reference 9

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Observation 0f5967ca-2e31-451f-b728-742f5cea1a9b · outbound

This paper cites Measuring the Quality of Text-to-Video Model Outputs: Metrics and Dataset.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Measuring the Quality of Text-to-Video Model Outputs: Metrics and Dataset

Reference 10

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Observation dd763d80-a736-4eb4-a1b0-b7f3680229aa · outbound

This paper cites Human- refiner: Benchmarking abnormal human generation and refin- ing with coarse-to-fine pose-reversible guidance.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Human- refiner: Benchmarking abnormal human generation and refin- ing with coarse-to-fine pose-reversible guidance

Reference 11

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Source-reported events for the cited work

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Observation 2e32aa43-580f-487c-af98-05b6d115fdf3 · outbound

This paper cites On the content bias in fréchet video distance.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts On the content bias in fréchet video distance

Reference 12

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Source-reported events for the cited work

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Observation 579c94bf-fdc7-4606-8990-cf09bc463afa · outbound

This paper cites YOLOX: Exceeding YOLO Series in 2021.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts YOLOX: Exceeding YOLO Series in 2021

Reference 13

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Observation c645143e-f963-4dc4-92c4-815f723f9986 · outbound

This paper cites Animatediff: Animate your personalized text-to-image diffusion models without specific tuning.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Animatediff: Animate your personalized text-to-image diffusion models without specific tuning

Reference 14

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Source-reported events for the cited work

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Observation 743a208e-3917-4eb8-b27b-da1175f1a0fe · outbound

This paper cites Deep Residual Learning for Image Recognition.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Deep Residual Learning for Image Recognition

Reference 15

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Observation 38b5cae5-8a68-411d-9d4e-a5f6033d5569 · outbound

This paper cites VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation

Reference 16

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Observation 4d8a2767-ecfb-46d2-b9bd-b0d879543023 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 17

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Source-reported events for the cited work

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Observation 70068352-7ae1-453b-a84b-bc0cec1b9742 · outbound

This paper cites Video diffusion models.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Video diffusion models

Reference 18

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Source-reported events for the cited work

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Observation 5778f97f-e515-4eeb-a3cb-0b8c87e7aaf9 · outbound

This paper cites Exddv: A new dataset for explainable deepfake detection in video.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Exddv: A new dataset for explainable deepfake detection in video

Reference 19

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Observation 90d2e361-3231-438c-a03e-8377e3835e06 · outbound

This paper cites Vbench: Comprehensive benchmark suite for video generative models.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Vbench: Comprehensive benchmark suite for video generative models

Reference 20

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Observation a9ee23a9-7e6b-4a28-acbc-17b0b9138df7 · outbound

This paper cites Text2video-zero: Text- to-image diffusion models are zero-shot video generators.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Text2video-zero: Text- to-image diffusion models are zero-shot video generators

Reference 21

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Observation 74d54ceb-a375-43fc-9c40-206ce6854001 · outbound

This paper cites Yolov11: An overview of the key architectural enhancements, 2024.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Yolov11: An overview of the key architectural enhancements, 2024

Reference 22

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Observation 09969874-95f4-4857-adef-64e444afe383 · outbound

This paper cites Pick-a-pic: An open dataset of user preferences for text-to-image generation.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Pick-a-pic: An open dataset of user preferences for text-to-image generation

Reference 23

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Observation 3848077f-0dd3-4de0-ad9c-11892e3a21dc · outbound

This paper cites Lawrence Zitnick.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Lawrence Zitnick

Reference 24

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Source-reported events for the cited work

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Observation d0128647-d639-48f7-8a55-09f80fb5b060 · outbound

This paper cites Improving Video Generation with Human Feedback.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Improving Video Generation with Human Feedback

Reference 25

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Observation 69843d71-c110-4575-9640-f31b7dcec319 · outbound

This paper cites Ntire 2024 quality assessment of ai-generated content challenge.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Ntire 2024 quality assessment of ai-generated content challenge

Reference 26

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Observation 62d5c62e-09dc-41f0-af70-2ea7ead95dcc · outbound

This paper cites Evalcrafter: Benchmarking and evalu- ating large video generation models.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Evalcrafter: Benchmarking and evalu- ating large video generation models

Reference 27

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This paper cites Fetv: A benchmark for fine-grained evaluation of open-domain text-to-video gen- eration.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Fetv: A benchmark for fine-grained evaluation of open-domain text-to-video gen- eration

Reference 28

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This paper cites Rt-detrv2: Improved baseline with bag-of-freebies for real-time detection transformer, 2024.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Rt-detrv2: Improved baseline with bag-of-freebies for real-time detection transformer, 2024

Reference 29

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This paper cites Gener- alizable deepfake detection with phase-based motion analysis.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Gener- alizable deepfake detection with phase-based motion analysis

Reference 30

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This paper cites Deepfake detection: A systematic literature review.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Deepfake detection: A systematic literature review

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This paper cites You only look once: Unified, real-time object de- tection.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts You only look once: Unified, real-time object de- tection

Reference 32

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GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Faster r-cnn: Towards real-time object detection with region proposal networks

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GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Fvd: A new metric for video generation

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This paper cites Towards Accurate Generative Models of Video: A New Metric & Challenges.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Towards Accurate Generative Models of Video: A New Metric & Challenges

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This paper cites GIT: A Generative Image-to-text Transformer for Vision and Language.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts GIT: A Generative Image-to-text Transformer for Vision and Language

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This paper cites Detecting Human Artifacts from Text-to-Image Models.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Detecting Human Artifacts from Text-to-Image Models

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GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Vidprom: A million-scale real prompt-gallery dataset for text-to-video diffusion models

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GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Image quality assessment: from error visibility to structural similarity

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GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Is this generated person existed in real-world? fine-grained detecting and calibrating abnormal human-body

Reference 40

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GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Wang, Fred Hohman, and Duen Horng Chau

Reference 41

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This paper cites Human preference score: Better aligning text-to- image models with human preference.

GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Human preference score: Better aligning text-to- image models with human preference

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GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts A survey on video diffusion models

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GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Imagereward: Learning and evaluating human preferences for text-to-image generation

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GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Ipo: Iterative prefer- ence optimization for text-to-video generation

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GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts The unreasonable effectiveness of deep features as a perceptual metric

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GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts Detrs beat yolos on real-time object detection, 2023

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