Paper Citation Record · LEDGER

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data

As of 21 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2411.10164.

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

pith.paper-citation-record.v1
2411.10164 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:56:48.533102Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T23:17:29.856464Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T23:17:34.753358Z

Reference resolution

41 of 41 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8c211d09-f2a4-4ce4-9fb5-abbf0473a672 · outbound

This paper cites kpam: Keypoint af- fordances for category-level robotic manipulation,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data kpam: Keypoint af- fordances for category-level robotic manipulation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:49.238339Z

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Observation 266ed5e3-210a-452f-83a4-69a18fefdf70 · outbound

This paper cites Learning hand-eye coordination for robotic grasping with deep learning and large- scale data collection,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Learning hand-eye coordination for robotic grasping with deep learning and large- scale data collection,

Reference 2

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Observation 574f4599-cb08-4da0-9748-aef6c546bf38 · outbound

This paper cites Interactive language: Talking to robots in real time,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Interactive language: Talking to robots in real time,

Reference 3

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Observation 5ecec224-d18e-4697-9d67-918fec80aebf · outbound

This paper cites Real-world robot applications of foundation models: a review,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Real-world robot applications of foundation models: a review,

Reference 4

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Observation 7647efd6-8a7a-4974-a032-0fcdff07c744 · outbound

This paper cites A review of synthetic image data and its use in computer vision,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data A review of synthetic image data and its use in computer vision,

Reference 5

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Observation fb9385bf-87ed-4d66-a20f-9fe5a81de893 · outbound

This paper cites Photorealistic image synthesis for object instance detection,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Photorealistic image synthesis for object instance detection,

Reference 6

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Source-reported events for the cited work

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Observation ecc9154b-b8d8-4f1c-be46-9bac15ae4a37 · outbound

This paper cites Fake it till you make it: face analysis in the wild using synthetic data alone,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Fake it till you make it: face analysis in the wild using synthetic data alone,

Reference 7

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Observation c21a2b99-ffb7-40c9-85bf-086d6e5cdfcb · outbound

This paper cites replicant: a pipeline for generating annotated images of animals in complex environments using unreal engine,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data replicant: a pipeline for generating annotated images of animals in complex environments using unreal engine,

Reference 8

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Observation 7275b7b0-6262-4ffe-9be0-a12041cdbd10 · outbound

This paper cites Learning dexterous in-hand manipulation,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Learning dexterous in-hand manipulation,

Reference 9

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Observation 09e7d21a-715c-46e2-8549-bdf8e1a2ae18 · outbound

This paper cites Learning keypoints for robotic cloth manipulation using synthetic data,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Learning keypoints for robotic cloth manipulation using synthetic data,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:49.095481Z

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Observation d861b94a-e8de-40ab-a689-facf08289d9f · outbound

This paper cites Scaling robot learning with seman- tically imagined experience,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Scaling robot learning with seman- tically imagined experience,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:49.077726Z

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Observation 10ce06bf-2e5c-42e6-ba93-7c5948849fd4 · outbound

This paper cites Genaug: Retargeting behaviors to unseen situations via generative augmentation,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Genaug: Retargeting behaviors to unseen situations via generative augmentation,

Reference 12

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Observation 9222dbe6-75e0-4621-b1dc-4b620e663573 · outbound

This paper cites DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery

Reference 13

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Observation f6d54c85-c1cd-4f94-9c0a-23ef77eff6e1 · outbound

This paper cites Adding conditional control to text-to-image diffusion models,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Adding conditional control to text-to-image diffusion models,

Reference 14

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Observation 31950538-b024-4968-8b9f-92dc47039c7c · outbound

This paper cites Generating Images with 3D Annotations Using Diffusion Models.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Generating Images with 3D Annotations Using Diffusion Models

Reference 15

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Observation 73289ed1-2ab0-43ce-ab67-d60fe5f2c985 · outbound

This paper cites Training deep networks with synthetic data: Bridging the reality gap by domain randomization,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Training deep networks with synthetic data: Bridging the reality gap by domain randomization,

Reference 16

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Observation 3ffa5602-6841-4c51-8a01-80f11d34efc5 · outbound

This paper cites Retinagan: An object-aware approach to sim-to-real transfer,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Retinagan: An object-aware approach to sim-to-real transfer,

Reference 17

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Source-reported events for the cited work

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Observation 0027bfc6-c988-400e-956f-a1155c9bed0a · outbound

This paper cites This dataset does not exist: training models from generated images,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data This dataset does not exist: training models from generated images,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:48.999405Z

Source-reported events for the cited work

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Observation 1b07152f-1011-4dd3-a91c-cd2b76e931fd · outbound

This paper cites Generative adversarial networks,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Generative adversarial networks,

Reference 19

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Observation f2391003-5859-47da-874c-93b027c393ff · outbound

This paper cites Datasetgan: Efficient labeled data factory with minimal human effort,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Datasetgan: Efficient labeled data factory with minimal human effort,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:56:48.966083Z

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Observation adac5929-bb5f-4a5c-94c9-ab1ad0eed27e · outbound

This paper cites Denoising diffusion probabilistic models,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Denoising diffusion probabilistic models,

Reference 21

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Observation 2500ba33-7cfd-448d-a903-096f8f64e834 · outbound

This paper cites Effective data augmentation with diffusion models,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Effective data augmentation with diffusion models,

Reference 23

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Observation 5fbe0d7f-9460-4dca-bc2c-66f5790ee5e1 · outbound

This paper cites Scaling laws of synthetic images for model training... for now,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Scaling laws of synthetic images for model training... for now,

Reference 24

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Observation 16870f1e-2738-4e2e-b700-4f34f62a9937 · outbound

This paper cites The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better

Reference 25

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Observation 79bd0a71-212b-4061-88f2-b66ad03840a8 · outbound

This paper cites Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic seg- mentation,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic seg- mentation,

Reference 26

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Observation b5fcb1bf-6940-451a-8681-a9cca86e456d · outbound

This paper cites Diffumask: Syn- thesizing images with pixel-level annotations for semantic segmentation using diffusion models,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Diffumask: Syn- thesizing images with pixel-level annotations for semantic segmentation using diffusion models,

Reference 27

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Observation 052b5270-c2b3-4f93-a552-ded4b17ce806 · outbound

This paper cites Medical diffusion on a budget: Textual Inversion for medical image generation.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Medical diffusion on a budget: Textual Inversion for medical image generation

Reference 28

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Observation f1b30195-2b10-48ef-aed3-57c82369d44a · outbound

This paper cites An image is worth one word: Personalizing text-to- image generation using textual inversion,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data An image is worth one word: Personalizing text-to- image generation using textual inversion,

Reference 29

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Observation 626e6b06-ed70-47bf-a0d5-032c07c0336d · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data High- resolution image synthesis with latent diffusion models,

Reference 30

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Observation 0266a344-abcf-4f33-a37b-224f045c4e85 · outbound

This paper cites Objaverse: A universe of annotated 3d objects,.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Objaverse: A universe of annotated 3d objects,

Reference 31

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Observation c07abb21-2717-467c-9f5c-3ad9b0c6e7ef · outbound

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Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Google scanned objects: A high- quality dataset of 3d scanned household items,

Reference 32

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Observation 350ea83b-fa15-4799-9a26-f4630ff05232 · outbound

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Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Blender - a 3d modelling and rendering package,

Reference 33

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Observation 7b826402-fa58-4c11-b9c0-38ae0f5aa30a · outbound

This paper cites Diffusers: State-of-the-art diffusion models.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Diffusers: State-of-the-art diffusion models

Reference 34

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Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data S3k: Self-supervised semantic keypoints for robotic manipulation via multi-view consistency,

Reference 35

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Observation 6926c02a-556f-4037-840c-9f5793c752c0 · outbound

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Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Objects as Points

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Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data U-net: Convolutional networks for biomedical image segmentation,

Reference 37

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Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Maxvit: Multi-axis vision transformer,

Reference 38

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Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Ultralytics yolov8,

Reference 39

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Observation a779b39d-d7d4-49c2-aa07-d4c91027b078 · outbound

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Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Microsoft coco: Common objects in context,

Reference 40

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This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 41

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Observation 63007d8f-2bb4-48f6-93c5-b8571b177d70 · outbound

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Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,

Reference 42

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Pith citing papers

Observation 35b1deb0-fc23-4a44-ba58-2da058bc729a · inbound

Follow-Your-Instruction: A Comprehensive MLLM Agent for World Data Synthesis cites this paper.

Follow-Your-Instruction: A Comprehensive MLLM Agent for World Data Synthesis Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data

Reference 23

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