Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction | Paper Citation Record

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This paper cites JAMA network open (2020) 4.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction JAMA network open (2020) 4

Reference 1

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This paper cites ACM Transac- tions on Graphics (TOG)42(4), 1–11 (2023) 3, 9, 10.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction ACM Transac- tions on Graphics (TOG)42(4), 1–11 (2023) 3, 9, 10

Reference 2

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 3

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This paper cites In: International Conference on Learning Representations (2021) 3.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: International Conference on Learning Representations (2021) 3

Reference 4

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This paper cites In: ICCV (2017) 9.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: ICCV (2017) 9

Reference 5

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This paper cites In: Proceedings of the IEEE/CVF international conference on computer vision.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF international conference on computer vision

Reference 6

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 7

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This paper cites In: Proceedings of the 28th ACM International Conference on Multimedia.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the 28th ACM International Conference on Multimedia

Reference 8

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This paper cites LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation

Reference 9

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This paper cites Consistent Video-to-Video Transfer Using Synthetic Dataset.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Consistent Video-to-Video Transfer Using Synthetic Dataset

Reference 10

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This paper cites Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Rethinking The Training And Evaluation of Rich-Context Layout-to-Image Generation

Reference 11

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This paper cites In: IEEE Conf.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: IEEE Conf

Reference 12

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This paper cites Scaling Instruction-Finetuned Language Models.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Scaling Instruction-Finetuned Language Models

Reference 13

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This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 14

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This paper cites In: Proceedings of the European conference on computer vision (ECCV).

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the European conference on computer vision (ECCV)

Reference 15

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This paper cites In: The Eleventh International Conference on Learning Representations (2022) 2, 4, 10 8 X.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: The Eleventh International Conference on Learning Representations (2022) 2, 4, 10 8 X

Reference 16

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This paper cites ACM Transactions on Graphics (TOG)42(4), 1–13 (2023) 2, 4.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction ACM Transactions on Graphics (TOG)42(4), 1–13 (2023) 2, 4

Reference 17

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This paper cites ACM Transactions on Graphics (TOG)42(4), 1–13 (2023) 6.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction ACM Transactions on Graphics (TOG)42(4), 1–13 (2023) 6

Reference 18

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Unresolved cited work

Reference 19

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This paper cites In: Proceeding of Thirty-eighth Conference on Neural Information Processing Systems.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceeding of Thirty-eighth Conference on Neural Information Processing Systems

Reference 20

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: AAAI (2019) 4

Reference 21

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This paper cites In: International Journal of Computer Vision (December 2024) 14.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: International Journal of Computer Vision (December 2024) 14

Reference 22

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This paper cites In: In Proceeding of IEEE Computer Vision and Pattern Recognition (2023) 14.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: In Proceeding of IEEE Computer Vision and Pattern Recognition (2023) 14

Reference 23

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This paper cites In: ECCV (2022) 4.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: ECCV (2022) 4

Reference 24

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This paper cites A Generalist FaceX via Learning Unified Facial Representation.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction A Generalist FaceX via Learning Unified Facial Representation

Reference 25

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This paper cites Advances in Neural Information Processing Systems33, 6840–6851 (2020) 5, 14.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Advances in Neural Information Processing Systems33, 6840–6851 (2020) 5, 14

Reference 26

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition

Reference 27

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: CVPR

Reference 29

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: CVPR (2020) 2

Reference 30

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This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 31

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 32

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition

Reference 33

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This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 34

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: International Con- ference on Machine Learning

Reference 35

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction CVPR (2020) 2, 14

Reference 36

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 37

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2024) 1, 4, 11

Reference 38

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision

Reference 39

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This paper cites Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation

Reference 40

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion

Reference 41

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Retrieved August15(2018), 11 (2018) 8

Reference 42

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This paper cites Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending

Reference 43

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This paper cites PortraitBooth: A Versatile Portrait Model for Fast Identity-preserved Personalization.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction PortraitBooth: A Versatile Portrait Model for Fast Identity-preserved Personalization

Reference 44

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: International conference on machine learning

Reference 45

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: CVPR (2022) 11

Reference 46

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: MICCAI

Reference 47

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: ICCV (2019) 14

Reference 48

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This paper cites In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2018) 8, 12.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2018) 8, 12

Reference 49

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 50

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This paper cites Advances in Neural Information Processing Systems35, 25278–25294 (2022) 2 10 X.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Advances in Neural Information Processing Systems35, 25278–25294 (2022) 2 10 X

Reference 51

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This paper cites In: NeurIPS (2024) 14.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: NeurIPS (2024) 14

Reference 52

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This paper cites In: Proceedings of the Euro- pean conference on computer vision (ECCV).

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the Euro- pean conference on computer vision (ECCV)

Reference 53

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This paper cites Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion

Reference 54

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction StableIdentity: Inserting Anybody into Anywhere at First Sight

Reference 55

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction InstantID: Zero-shot Identity-Preserving Generation in Seconds

Reference 56

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021) 8

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:04:28.675249Z

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Observation 83951a62-57d2-4af0-9a98-8e950ee35ad7 · outbound

This paper cites HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping

Reference 58

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation

Reference 59

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 60

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Observation 310f33fb-3046-49d2-81da-723dbdbe5d7a · outbound

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: European Conference on Computer Vision

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:04:28.658011Z

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 62

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Observation ca3198af-0d74-44ef-b46f-e0f5e21b5927 · outbound

This paper cites Foundations and Trends in Privacy and Security (January 2024) 14.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Foundations and Trends in Privacy and Security (January 2024) 14

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:04:28.608202Z

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Reference 64

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Observation 355e320a-837e-46e8-8e78-1974d2d449f2 · outbound

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Learning Face Representation from Scratch

Reference 65

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Observation 907cfb8e-f2d1-4d5a-98ee-cd3c7b951f57 · outbound

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Advances in Neural Information Processing Systems36 (2024) 3

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:04:28.575737Z

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Observation 38e10782-ba52-4b24-847c-3b6da4fb61e5 · outbound

This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:04:28.559795Z

Source-reported events for the cited work

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Observation 5f1281e6-66a5-4107-bc48-42754c247592 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction OPT: Open Pre-trained Transformer Language Models

Reference 68

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Observation 12c08b92-bf81-488a-a2b3-2b4a6b5eafb4 · outbound

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model

Reference 69

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Observation 7c2938de-a077-4fec-8b31-e551546a72e2 · outbound

This paper cites In: European Conference on Computer Vision (2025) 14.

Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction In: European Conference on Computer Vision (2025) 14

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:04:28.543209Z

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Observation d759467c-ceb7-408f-bc94-889b65c0183a · outbound

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Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction Unresolved cited work

Reference 177

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Outbound references

Pith citing papers