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Awesome-ICCV2021-Low-Level-Vision

A Collection of Papers and Codes for ICCV2021 Low Level Vision and Image Generation

整理汇总下2021年ICCV中图像生成（Image Generation）和底层视觉(Low-Level Vision)任务相关的论文和代码，包括图像生成，图像编辑，图像风格迁移，图像翻译，图像修复，图像超分及其他底层视觉任务。大家如果觉得有帮助，欢迎star~~

参考或转载请注明出处,文中有不足或者需要补充的地方也欢迎PR

ICCV2021官网：https://iccv2021.thecvf.com/

ICCV2021完整论文列表：https://openaccess.thecvf.com/ICCV2021

开会时间：2021年10月11日-10月17日

【Contents】

• 1.图像生成（Image Generation）
• 2.图像编辑（Image Manipulation/Image Editing）
• 3.图像风格迁移（Image Transfer）
• 4.图像翻译（Image to Image Translation）
• 5.图像修复（Image Inpaiting/Image Completion）
• 6.图像超分辨率（Image Super-Resolution）
• 7.图像去雨（Image Deraining）
• 8.图像去雾（Image Dehazing）
• 9.图像去模糊（Image Deblurring）
• 10.图像去噪（Image Denoising）
• 11.图像恢复（Image Restoration）
• 12.图像增强（Image Enhancement）
• 13.图像质量评价（Image Quality Assessment）
• 14.插帧（Frame Interpolation）
• 15.视频/图像压缩（Video/Image Compression）
• 16.其他底层视觉任务（Other Low Level Vision）

<a name="1.图像生成"></a>

1.图像生成（Image Generation）

Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts

• Paper：https://arxiv.org/abs/2104.00887
• Code：https://github.com/clovaai/mxfont
• 小样本字体生成

PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering

• Code：https://github.com/RenYurui/PIRender

Toward Spatially Unbiased Generative Models

• Code：https://github.com/jychoi118/toward_spatial_unbiased

Disentangled Lifespan Face Synthesis

• Paper：https://arxiv.org/abs/2108.02874
• Code：https://github.com/clovaai/mxfont

Handwriting Transformers

• Paper：https://arxiv.org/abs/2104.03964
• Code：https://github.com/ankanbhunia/Handwriting-Transformers

Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Translation

• Paper：https://arxiv.org/abs/2103.16146

ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement

• Paper：https://arxiv.org/abs/2104.02699
• Code：https://github.com/yuval-alaluf/restyle-encoder

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction

• Paper：https://arxiv.org/abs/2108.03798
• Code：https://github.com/huage001/painttransformer

GAN Inversion for Out-of-Range Images with Geometric Transformations

• Paper：https://arxiv.org/abs/2108.08998

The Animation Transformer: Visual Correspondence via Segment Matching

• Paper：https://arxiv.org/abs/2109.02614
• 手绘图变动画

Image Synthesis via Semantic Composition

• Paper：https://shepnerd.github.io/scg/resources/01145.pdf
• Code：https://github.com/dvlab-research/SCGAN

Detail Me More: Improving GAN's Photo-Realism of Complex Scenes

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Gadde_Detail_Me_More_Improving_GANs_Photo-Realism_of_Complex_Scenes_ICCV_2021_paper.html

De-Rendering Stylized Texts

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Shimoda_De-Rendering_Stylized_Texts_ICCV_2021_paper.html
• Code：https://github.com/dvlab-research/SCGAN

<a name="2.图像编辑"></a>

2.图像编辑（Image Manipulation/Image Editing）

EigenGAN: Layer-Wise Eigen-Learning for GANs

• Paper：https://arxiv.org/abs/2104.12476
• Code：https://github.com/LynnHo/EigenGAN-Tensorflow

From Continuity to Editability: Inverting GANs with Consecutive Images

• Paper：https://arxiv.org/abs/2107.13812
• Code：https://github.com/cnnlstm/InvertingGANs_with_ConsecutiveImgs

HeadGAN: One-shot Neural Head Synthesis and Editing

• Paper：https://arxiv.org/abs/2012.08261

Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation

• Code：https://github.com/csyxwei/OroJaR

Sketch Your Own GAN

• Paper：https://arxiv.org/abs/2108.02774
• Code：https://github.com/PeterWang512/GANSketching

A Latent Transformer for Disentangled Face Editing in Images and Videos

• Paper：https://arxiv.org/abs/2106.11895
• Code：https://github.com/InterDigitalInc/Latent-Transformer

Learning Facial Representations from the Cycle-consistency of Face

• Paper：https://arxiv.org/abs/2108.03427
• Code：https://github.com/jiarenchang/facecycle

StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery

• Paper：https://arxiv.org/abs/2103.17249
• Code：https://github.com/orpatashnik/StyleCLIP

Talk-to-Edit: Fine-Grained Facial Editing via Dialog

• Paper：https://arxiv.org/abs/2109.04425
• Code：https://github.com/yumingj/Talk-to-Edit

Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing

• Paper：https://cuiaiyu.github.io/dressing-in-order/Cui_Dressing_in_Order.pdf
• Code：https://github.com/cuiaiyu/dressing-in-order

GAN-Control: Explicitly Controllable GANs

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Shoshan_GAN-Control_Explicitly_Controllable_GANs_ICCV_2021_paper.html
• Code：https://github.com/cuiaiyu/dressing-in-order

Explaining in Style: Training a GAN To Explain a Classifier in StyleSpace

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Lang_Explaining_in_Style_Training_a_GAN_To_Explain_a_Classifier_ICCV_2021_paper.html
• Code：https://github.com/google/explaining-in-style

<a name="3.图像风格迁移"></a>

3.图像风格迁移（Image Transfer）

ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity

• Paper：https://arxiv.org/abs/2103.09776

Domain Aware Universal Style Transfer

• Paper：https://arxiv.org/abs/2108.04441
• Code：https://github.com/Kibeom-Hong/Domain-Aware-Style-Transfer

AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer

• Paper：https://arxiv.org/abs/2108.03647
• Code：https://github.com/Huage001/AdaAttN

Diverse Image Style Transfer via Invertible Cross-Space Mapping

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Chen_Diverse_Image_Style_Transfer_via_Invertible_Cross-Space_Mapping_ICCV_2021_paper.html

StyleFormer: Real-Time Arbitrary Style Transfer via Parametric Style Composition

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Wu_StyleFormer_Real-Time_Arbitrary_Style_Transfer_via_Parametric_Style_Composition_ICCV_2021_paper.html

<a name="4.图像翻译"></a>

4.图像翻译（Image to Image Translation）

SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation

• Paper：https://arxiv.org/abs/2103.16219
• Code：https://github.com/NetEase-GameAI/SPatchGAN

Scaling-up Disentanglement for Image Translation

• Paper：https://arxiv.org/abs/2103.14017
• Code：https://github.com/avivga/overlord

Unaligned Image-to-Image Translation by Learning to Reweight

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Xie_Unaligned_Image-to-Image_Translation_by_Learning_to_Reweight_ICCV_2021_paper.html
• Code：https://github.com/Mid-Push/IrwGAN <a name="5.图像修复"></a>

5.图像修复（Image Inpaiting/Image Completion）

Implicit Internal Video Inpainting

• Code：https://github.com/Tengfei-Wang/Implicit-Internal-Video-Inpainting

Internal Video Inpainting by Implicit Long-range Propagation

• Code：https://github.com/Tengfei-Wang/Annotated-4K-Videos

Occlusion-Aware Video Object Inpainting

• Paper：https://arxiv.org/abs/2108.06765

High-Fidelity Pluralistic Image Completion with Transformers

• Paper：https://arxiv.org/abs/2103.14031
• Code：https://github.com/raywzy/ICT

Image Inpainting via Conditional Texture and Structure Dual Generation

• Paper：https://arxiv.org/abs/2108.09760v1
• Code：https://github.com/Xiefan-Guo/CTSDG

CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction

• Paper：https://arxiv.org/abs/2011.12836
• Code：https://github.com/zengxianyu/crfill

FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Liu_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting_ICCV_2021_paper.html
• Code：https://github.com/ruiliu-ai/FuseFormer

<a name="6.图像超分辨率"></a>

6.图像超分辨率（Image Super-Resolution）

Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution

• Code：https://github.com/JingyunLiang/MANet

Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling

• Code：https://github.com/JingyunLiang/HCFlow

Deep Blind Video Super-resolution

• Code：https://github.com/csbhr/Deep-Blind-VSR

Omniscient Video Super-Resolution

• Code：https://github.com/psychopa4/OVSR

Learning A Single Network for Scale-Arbitrary Super-Resolution

• Paper：https://arxiv.org/abs/2004.03791
• Code：https://github.com/LongguangWang/ArbSR

Deep Reparametrization of Multi-Frame Super-Resolution and Denoising

• Paper：https://arxiv.org/abs/2108.08286

Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts

• Paper：https://arxiv.org/abs/2104.06191

Attention-Based Multi-Reference Learning for Image Super-Resolution

• Paper：https://openaccess.thecvf.com/content/ICCV2021/papers/Pesavento_Attention-Based_Multi-Reference_Learning_for_Image_Super-Resolution_ICCV_2021_paper.pdf

Fourier Space Losses for Efficient Perceptual Image Super-Resolution

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Fuoli_Fourier_Space_Losses_for_Efficient_Perceptual_Image_Super-Resolution_ICCV_2021_paper.html

COMISR: Compression-Informed Video Super-Resolution

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Li_COMISR_Compression-Informed_Video_Super-Resolution_ICCV_2021_paper.html
• Code：https://github.com/google-research/google-research/tree/master/comisr
• 针对压缩后的视频超分

Designing a Practical Degradation Model for Deep Blind Image Super-Resolutio

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Zhang_Designing_a_Practical_Degradation_Model_for_Deep_Blind_Image_Super-Resolution_ICCV_2021_paper.html
• Code：https://github.com/cszn/BSRGAN

Event Stream Super-Resolution via Spatiotemporal Constraint Learning

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Li_Event_Stream_Super-Resolution_via_Spatiotemporal_Constraint_Learning_ICCV_2021_paper.html

Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Li_Super-Resolving_Cross-Domain_Face_Miniatures_by_Peeking_at_One-Shot_Exemplar_ICCV_2021_paper.html

Attention-based Multi-Reference Learning for Image Super-Resolution

• Paper：https://arxiv.org/abs/2108.13697
• Code：https://github.com/marcopesavento/Attention-based-Multi-Reference-Learning-for-Image-Super-Resolution

<a name="7.图像去雨"></a>

7.图像去雨（Image Deraining）

Structure-Preserving Deraining with Residue Channel Prior Guidance

• Code：https://github.com/Joyies/SPDNet

Improving De-Raining Generalization via Neural Reorganization

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Xiao_Improving_De-Raining_Generalization_via_Neural_Reorganization_ICCV_2021_paper.html
• Code：https://github.com/cszn/BSRGAN

Unpaired Learning for Deep Image Deraining With Rain Direction Regularizer

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Unpaired_Learning_for_Deep_Image_Deraining_With_Rain_Direction_Regularizer_ICCV_2021_paper.html
• Code：https://github.com/cszn/BSRGAN

<a name="8.图像去雾"></a>

8.图像去雾（Image Dehazing）

<a name="9.去模糊"></a>

9.图像去模糊（Image Deblurring）

Bringing Events into Video Deblurring with Non consecutively Blurry Frames

• Code：https://github.com/shangwei5/D2Net

Rethinking Coarse-to-Fine Approach in Single Image Deblurring

• Paper：https://arxiv.org/abs/2108.05054
• Code：https://github.com/chosj95/MIMO-UNet

Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Son_Single_Image_Defocus_Deblurring_Using_Kernel-Sharing_Parallel_Atrous_Convolutions_ICCV_2021_paper.html

<a name="10.去噪"></a>

10.图像去噪（Image Denoising）

C2N: Practical Generative Noise Modeling for Real-World Denoising

• Paper：https://openaccess.thecvf.com/content/ICCV2021/papers/Jang_C2N_Practical_Generative_Noise_Modeling_for_Real-World_Denoising_ICCV_2021_paper.pdf

Self-Supervised Image Prior Learning With GMM From a Single Noisy Image

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Self-Supervised_Image_Prior_Learning_With_GMM_From_a_Single_Noisy_ICCV_2021_paper.html
• Code：https://github.com/HUST-Tan/SS-GMM

<a name="11.图像恢复"></a>

11.图像恢复（Image Restoration）

Spatially-Adaptive Image Restoration using Distortion-Guided Networks

• Paper：https://arxiv.org/abs/2108.08617

Dynamic Attentive Graph Learning for Image Restoration

• Paper：https://arxiv.org/abs/2109.06620
• Code：https://github.com/jianzhangcs/DAGL

<a name="12.图像增强"></a>

12.图像增强（Image Enhancement）

StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement

• Paper：https://arxiv.org/abs/2107.12898
• Code：https://github.com/IDKiro/StarEnhancer

Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables

• Paper：https://arxiv.org/abs/2108.08697

Representative Color Transform for Image Enhancement

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Kim_Representative_Color_Transform_for_Image_Enhancement_ICCV_2021_paper.html

Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Zheng_Adaptive_Unfolding_Total_Variation_Network_for_Low-Light_Image_Enhancement_ICCV_2021_paper.html
• Code：https://github.com/YU-Zhiyang/WEVI

<a name="13.图像质量评价"></a>

13.图像质量评价（Image Quality Assessment）

MUSIQ: Multi-scale Image Quality Transformer

• Paper：https://arxiv.org/abs/2108.05997

<a name="14.插帧"></a>

14.插帧（Frame Interpolation）

XVFI: eXtreme Video Frame Interpolation

• Paper：https://arxiv.org/abs/2103.16206
• Code：https://github.com/JihyongOh/XVFI

Asymmetric Bilateral Motion Estimation for Video Frame Interpolation

• Paper： https://arxiv.org/abs/2108.06815
• Code： https://github.com/JunHeum/ABME

Training Weakly Supervised Video Frame Interpolation With Events

• Paper： https://openaccess.thecvf.com/content/ICCV2021/html/Yu_Training_Weakly_Supervised_Video_Frame_Interpolation_With_Events_ICCV_2021_paper.html

<a name="15.视频压缩"></a>

15.视频/图像压缩（Video/Image Compression）

Extending Neural P-frame Codecs for B-frame Coding

• Paper：https://arxiv.org/abs/2104.00531

Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform

• Paper：https://arxiv.org/abs/2108.09551
• Code：https://github.com/micmic123/QmapCompression

Efficient Video Compression via Content-Adaptive Super-Resolution

• Paper：https://openaccess.thecvf.com/content/ICCV2021/papers/Khani_Efficient_Video_Compression_via_Content-Adaptive_Super-Resolution_ICCV_2021_paper.pdf
• Code：https://github.com/AdaptiveVC/SRVC

<a name="16.其他底层视觉任务"></a>

16.其他底层视觉任务（Other Low Level Vision）

Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation

• Code：https://github.com/Anonymous-iccv2021-paper3163/CaFM-Pytorch
• 视频传输

Focal Frequency Loss for Image Reconstruction and Synthesis

• Paper：https://arxiv.org/abs/2012.12821
• Code：https://github.com/EndlessSora/focal-frequency-loss
• 频域损失，补充空域损失的不足

ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss

• Code：https://github.com/weitingchen83/ICCV2021-Single-Image-Desnowing-HDCWNet

IICNet: A Generic Framework for Reversible Image Conversion

• Code：https://github.com/felixcheng97/IICNet

Self-Conditioned Probabilistic Learning of Video Rescaling

• Paper：https://arxiv.org/abs/2107.11639

HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset

• Paper：https://arxiv.org/abs/2103.14943
• Code：https://github.com/guanyingc/DeepHDRVideo

A New Journey from SDRTV to HDRTV

• Paper：https://arxiv.org/abs/2108.07978
• Code：https://github.com/chxy95/HDRTVNet

SSH: A Self-Supervised Framework for Image Harmonization

• Paper：https://arxiv.org/abs/2108.06805
• Code：https://github.com/VITA-Group/SSHarmonization

Towards Vivid and Diverse Image Colorization with Generative Color Prior

• Paper：https://arxiv.org/abs/2108.08826

Towards Flexible Blind JPEG Artifacts Removal

• Paper：https://github.com/jiaxi-jiang/FBCNN/releases/download/v1.0/FBCNN_ICCV2021.pdf
• Code：https://github.com/jiaxi-jiang/FBCNN

Location-Aware Single Image Reflection Removal

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Dong_Location-Aware_Single_Image_Reflection_Removal_ICCV_2021_paper.html
• Code：https://github.com/zdlarr/Location-aware-SIRR

Learning To Remove Refractive Distortions From Underwater Images

• Paper：https://openaccess.thecvf.com/content/ICCV2021/html/Thapa_Learning_To_Remove_Refractive_Distortions_From_Underwater_Images_ICCV_2021_paper.html

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