F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration | Paper Citation Record

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Unresolved cited work

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration co / dreamlike - art / dreamlike - photoreal - 2

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration co / black - forest-labs/FLUX.1-dev, Accessed: 2024-10-03

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration co/h94/IP-Adapter, Accessed: 2024-10-03

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration co/h94/IP-Adapter-FaceID, Accessed: 2024-10-

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration co / Kwai - Kolors/Kolors, Accessed: 2024-10-03

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration co / stablediffusionapi/protovision- xl- v6.6, Accessed: 2024-10-03

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration 3, 5, 1, 4

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Unresolved cited work

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Qwen2.5-VL Technical Report

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Cr-fiqa: face image quality assessment by learning sample relative classifiability

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration An image quality assessment dataset for portraits

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Promptiqa: Boosting the performance and gen- eralization for no-reference image quality assessment via prompts

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Dsl-fiqa: Assess- ing facial image quality via dual-set degradation learning and landmark-guided transformer

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F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Flashattention-2: Faster attention with better par- allelism and work partitioning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:28:11.088744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 29e06a82-962a-450d-a100-f919021289f2 · outbound

This paper cites Enhanced blind face restoration with multi-exemplar images and adaptive spatial feature fusion.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Enhanced blind face restoration with multi-exemplar images and adaptive spatial feature fusion

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:28:11.073289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1f76afff-8075-4ccc-bba0-ed2d66ab3299 · outbound

This paper cites Learning dual memory dic- tionaries for blind face restoration.IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Learning dual memory dic- tionaries for blind face restoration.IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c62b50b8-e0ee-4cfe-a1e4-86293ca17358 · outbound

This paper cites PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbb84cfd-0e08-491d-b4e3-b9c6eb9be24d · outbound

This paper cites Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e77acbb4-375d-4d97-a053-ace6682d59c9 · outbound

This paper cites Rich human feedback for text-to-image generation.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Rich human feedback for text-to-image generation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:28:11.040248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f7985be2-099e-41ac-b15c-69375fc1fb51 · outbound

This paper cites Multi-branch face quality assess- ment for face recognition.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Multi-branch face quality assess- ment for face recognition

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c2d3026f-a922-463e-8e7a-d1730cc15240 · outbound

This paper cites Microsoft coco: Common objects in context.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Microsoft coco: Common objects in context

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b0f7b278-3ef2-4f80-a4cf-994cadb217f2 · outbound

This paper cites DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ba76694-cbe8-45d0-b8a1-65ad87f314e2 · outbound

This paper cites Visual instruction tuning.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Visual instruction tuning

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5fb7f03f-f539-49df-be64-b1f1a5de6f05 · outbound

This paper cites Blind quality assessment based on pseudo-reference image.IEEE Transactions on Multimedia (TMM), 20(8):2049–2062, 2018.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Blind quality assessment based on pseudo-reference image.IEEE Transactions on Multimedia (TMM), 20(8):2049–2062, 2018

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 05ee05b2-f3f8-4538-aba8-85685c0bae48 · outbound

This paper cites Blind image quality estimation via distortion aggravation.IEEE Transactions on Broadcasting, 64(2): 508–517, 2018.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Blind image quality estimation via distortion aggravation.IEEE Transactions on Broadcasting, 64(2): 508–517, 2018

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e496d7ac-65e4-4d7e-b1ed-87ea68cab0af · outbound

This paper cites Perceptual video quality assessment: A survey.Science China Information Sciences, 67(11): 211301, 2024.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Perceptual video quality assessment: A survey.Science China Information Sciences, 67(11): 211301, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:28:10.942036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8fa0ef26-b8dc-4bde-8d73-32d14d506a2b · outbound

This paper cites completely blind.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration completely blind

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:28:10.926036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3f150026-b016-4346-9169-01eb48018e9d · outbound

This paper cites Agedb: the first manually collected, in-the-wild age database.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Agedb: the first manually collected, in-the-wild age database

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:28:10.910619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a5c7c3aa-ab98-4628-9989-88672910132d · outbound

This paper cites Sdd-fiqa: Unsupervised face image quality assess- ment with similarity distribution distance.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Sdd-fiqa: Unsupervised face image quality assess- ment with similarity distribution distance

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5dc5d659-b074-4d50-b477-a71a5578b50e · outbound

This paper cites Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5eff706-fcc0-4971-98d5-96b679583d4e · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9ba476b-3721-4e51-a78d-973ff897679a · outbound

This paper cites Zero-shot text-to-image generation, 2021.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Zero-shot text-to-image generation, 2021

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 430798b7-59a4-41f3-a2a1-7130a6ad91ce · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Hierarchical Text-Conditional Image Generation with CLIP Latents

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Resolution: unresolved; no resolver link, observed 2026-08-11T13:28:09.431321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b219e2a-d6bb-43f2-912f-2aac25e6c5fd · outbound

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

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration High-resolution image synthesis with latent diffusion models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:28:10.868274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 10a8ebb6-1c63-408e-ae41-f0d1efc0ae37 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:28:10.853227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 17eac0fe-1216-400b-b494-f066942df403 · outbound

This paper cites Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8043c049-3521-4c8f-8d34-71d3832c1b75 · outbound

This paper cites an unresolved cited work.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Unresolved cited work

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 97d11490-da67-45ec-a919-a8c511bf78c1 · outbound

This paper cites Methodology for the subjective assessment of the quality of television pictures.Recommendation ITU-R BT, 500(13), 2012.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Methodology for the subjective assessment of the quality of television pictures.Recommendation ITU-R BT, 500(13), 2012

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a6904c97-8a60-4420-a220-32bbd693ab35 · outbound

This paper cites Photo uncrop.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Photo uncrop

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1b25537d-cc10-4411-81ff-7d3383eaac75 · outbound

This paper cites Responsible re- search with crowds: pay crowdworkers at least minimum wage.Communications of the ACM, 61(3):39–41, 2018.

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 03e9a3e7-41f5-407d-a2ef-363325f98b4c · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Very Deep Convolutional Networks for Large-Scale Image Recognition

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89abb9ad-b564-4d6c-8cbf-8c4d9f17ba3c · outbound

This paper cites Denoising Diffusion Implicit Models.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Denoising Diffusion Implicit Models

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63564549-019d-4c0f-b732-75ebf3a0f87e · outbound

This paper cites Blindly assess image qual- ity in the wild guided by a self-adaptive hyper network.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Blindly assess image qual- ity in the wild guided by a self-adaptive hyper network

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:28:10.763114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b8e09704-cafb-48c3-a194-12daec45bb1c · outbound

This paper cites Going the extra mile in face image quality assess- ment: A novel database and model.IEEE Transactions on Multimedia, 2023.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Going the extra mile in face image quality assess- ment: A novel database and model.IEEE Transactions on Multimedia, 2023

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 27856703-bcc6-48d4-8e9f-bbf4787d07ef · outbound

This paper cites Ser-fiq: Unsupervised estimation of face image quality based on stochastic embed- ding robustness.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Ser-fiq: Unsupervised estimation of face image quality based on stochastic embed- ding robustness

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b0480844-51a8-417c-a3b3-91a9262310b5 · outbound

This paper cites Towards all-in-one medical image re-identification.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Towards all-in-one medical image re-identification

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9a429446-321c-4795-8103-08a3df7947f1 · outbound

This paper cites Stylegan2 distillation for feed-forward image manipulation.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Stylegan2 distillation for feed-forward image manipulation

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1db0fb2a-5463-4095-9e15-0c525effa9b5 · outbound

This paper cites Aigciqa2023: A large-scale im- age quality assessment database for ai generated images: from the perspectives of quality, authenticity and corre- spondence.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Aigciqa2023: A large-scale im- age quality assessment database for ai generated images: from the perspectives of quality, authenticity and corre- spondence

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:28:10.690350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6a6d2ec4-ec1c-4725-b63b-969c7bad0bec · outbound

This paper cites Aigciqa2023: A large-scale im- age quality assessment database for ai generated images: from the perspectives of quality, authenticity and corre- spondence.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Aigciqa2023: A large-scale im- age quality assessment database for ai generated images: from the perspectives of quality, authenticity and corre- spondence

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ca570237-a40b-418a-ab7c-5d309b683ab0 · outbound

This paper cites Exploiting Diffusion Prior for Real-World Image Super-Resolution.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Exploiting Diffusion Prior for Real-World Image Super-Resolution

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Source-reported events for the cited work

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Observation 1693e052-5e6c-4277-b79c-a00331e13b7a · outbound

This paper cites Quality Assessment for AI Generated Images with Instruction Tuning.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Quality Assessment for AI Generated Images with Instruction Tuning

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec95d0f6-8897-455e-bf65-aaa801fb1f97 · outbound

This paper cites Chan, and Chen Change Loy.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Chan, and Chen Change Loy

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2499f1e3-77a4-44d5-a7fe-3292e6a04245 · outbound

This paper cites Xintao Wang, Honglun Zhang, Chao Dong, and Ying Shan.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Xintao Wang, Honglun Zhang, Chao Dong, and Ying Shan

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 74eb4c1a-26fa-4bce-8e42-472a7469a856 · outbound

This paper cites InstantID: Zero-shot Identity-Preserving Generation in Seconds.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration InstantID: Zero-shot Identity-Preserving Generation in Seconds

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf4b8864-95f0-4fbd-9571-81b248ee9d98 · outbound

This paper cites A survey of deep face restoration: Denoise, super-resolution, deblur, artifact re- moval.arXiv preprint arXiv:2211.02831, 2022.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration A survey of deep face restoration: Denoise, super-resolution, deblur, artifact re- moval.arXiv preprint arXiv:2211.02831, 2022

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dea9e9d8-5210-480f-99d1-72a8fcf62338 · outbound

This paper cites Real-esrgan: Training real-world blind super-resolution with pure synthetic data.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Real-esrgan: Training real-world blind super-resolution with pure synthetic data

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6ea9ac46-4b48-4f52-b74b-87ab8685b886 · outbound

This paper cites To- wards real-world blind face restoration with generative fa- cial prior.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration To- wards real-world blind face restoration with generative fa- cial prior

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5fae0384-2418-4eff-99c2-fe94ec794551 · outbound

This paper cites To- wards real-world blind face restoration with generative fa- cial prior.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration To- wards real-world blind face restoration with generative fa- cial prior

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a76a67ef-d970-46fe-826f-a93172ae61a9 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.IEEE Transactions on Image Pro- cessing (TIP), 13(4):600–612, 2004.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Image quality assessment: from error visibility to structural similarity.IEEE Transactions on Image Pro- cessing (TIP), 13(4):600–612, 2004

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 35a27293-0041-47f0-a6a4-601dea5f851c · outbound

This paper cites Dr2: Diffusion-based robust degradation remover for blind face restoration.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Dr2: Diffusion-based robust degradation remover for blind face restoration

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 91221b86-57f7-42ba-a94e-2695d509b31c · outbound

This paper cites Elite: Encoding visual con- cepts into textual embeddings for customized text-to-image generation.

F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration Elite: Encoding visual con- cepts into textual embeddings for customized text-to-image generation

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