VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution
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Cited by 42 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Most of the existing video face super-resolution (VFSR) methods are trained and evaluated on VoxCeleb1, which is designed specifically for speaker identification and the frames in this dataset are of low quality. As a consequence, the VFSR models trained on this dataset can not output visual-pleasing results. In this paper, we develop an automatic and scalable pipeline to collect a high-quality video face dataset (VFHQ), which contains over 16,000 high-fidelity clips of diverse interview scenarios. To verify the necessity of VFHQ, we further conduct experiments and demonstrate that VFSR models trained on our VFHQ dataset can generate results with sharper edges and finer textures than those trained on VoxCeleb1. In addition, we show that the temporal information plays a pivotal role in eliminating video consistency issues as well as further improving visual performance. Based on VFHQ, by analyzing the benchmarking study of several state-of-the-art algorithms under bicubic and blind settings. See our project page: https://liangbinxie.github.io/projects/vfhq
Key facts. Cited by 174 papers in the map (data through 7 Oct 2026). Builds on 33 of its 50 references that are in the map. First posted on arXiv 6 May 2022.
Builds on
33 of 50 references.
References · 33
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
- 2014Deep Learning Face Attributes in the Wildcited by 3,679
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
- 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
- 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
- 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
- 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
- 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
- 2016Simple Online and Realtime Trackingcited by 862
- 2018VoxCeleb2: Deep Speaker Recognitioncited by 862
- 2017VoxCeleb: a large-scale speaker identification datasetcited by 714
- 2017Video Enhancement with Task-Oriented Flowcited by 612
21 more
- 2019EDVR: Video Restoration with Enhanced Deformable Convolutional Networkscited by 492
- 2021Towards Real-World Blind Face Restoration with Generative Facial Priorcited by 347
- 2020BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyondcited by 303
- 2020Deep Unfolding Network for Image Super-Resolutioncited by 253
- 2018Learning Blind Video Temporal Consistencycited by 247
- 2018Deep Back-Projection Networks For Super-Resolutioncited by 224
- 2021GAN Prior Embedded Network for Blind Face Restoration in the Wildcited by 197
- 2017FSRNet: End-to-End Learning Face Super-Resolution with Facial Priorscited by 170
- 2019RetinaFace: Single-stage Dense Face Localisation in the Wildcited by 164
- 2019FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimationcited by 128
- 2019Adaptive Wing Loss for Robust Face Alignment via Heatmap Regressioncited by 127
- 2020Blind Face Restoration via Deep Multi-scale Component Dictionariescited by 106
- 2020Progressive Semantic-Aware Style Transformation for Blind Face Restorationcited by 99
- 2018Learning Warped Guidance for Blind Face Restorationcited by 87
- 2020Blind Video Temporal Consistency via Deep Video Priorcited by 76
- 2019Exemplar Guided Face Image Super-Resolution without Facial Landmarkscited by 57
- 2016Deep Cascaded Bi-Network for Face Hallucinationcited by 37
- 2021Deep Learning-based Face Super-Resolution: A Surveycited by 32
- 2019Learning to Have an Ear for Face Super-Resolutioncited by 11
- 2019Self-Enhanced Convolutional Network for Facial Video Hallucinationcited by 5
- 2020Video Face Super-Resolution with Motion-Adaptive Feedback Cellcited by 5
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Citing papers per year: 2022: 2; 2023: 7; 2024: 39; 2025: 79; 2026: 47.
174 papers.
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