Awesome-CVPR2023-Low-Level-Vision

A Collection of Papers and Codes in CVPR2023 related to Low-Level Vision

Related collections for low-level vision

• Awesome-CVPR2022-Low-Level-Vision
• Awesome-ECCV2022-Low-Level-Vision
• Awesome-AAAI2022-Low-Level-Vision
• Awesome-NeurIPS2021-Low-Level-Vision
• Awesome-ICCV2021-Low-Level-Vision
• Awesome-CVPR2021/CVPR2020-Low-Level-Vision
• Awesome-ECCV2020-Low-Level-Vision

Catalogue

• Image Restoration

  • Video Restoration

• Super Resolution

  • Image Super Resolution
  • Video Super Resolution

• Image Rescaling

• Denoising

  • Image Denoising
  • Video Denoising

• Deblurring

  • Image Deblurring
  • Video Deblurring

• Deraining

• Dehazing

• Demosaicing

• HDR Imaging / Multi-Exposure Image Fusion

• Frame Interpolation

• Image Enhancement

  • Low-Light Image Enhancement

• Image Harmonization

• Image Completion/Inpainting

• Image Matting

• Shadow Removal

• Image Compression

• Image Quality Assessment

• Style Transfer

• Image Editing

• Image Generation/Synthesis/ Image-to-Image Translation

  • Video Generation

• Others

Image Restoration - 图像恢复

Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

• Paper: https://arxiv.org/abs/2303.00748
• Code: https://github.com/ofsoundof/GRL-Image-Restoration
• Tags: Transformer

Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective

• Paper: https://arxiv.org/abs/2303.06859
• Code: https://github.com/lixinustc/Casual-IRDIL

Generative Diffusion Prior for Unified Image Restoration and Enhancement

• Paper: https://arxiv.org/abs/2304.01247
• Code: https://github.com/Fayeben/GenerativeDiffusionPrior

DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration

• Paper: https://arxiv.org/abs/2303.06885

Bitstream-Corrupted JPEG Images are Restorable: Two-stage Compensation and Alignment Framework for Image Restoration

• Paper: https://arxiv.org/abs/2304.06976
• Code: https://github.com/wenyang001/Two-ACIR

Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank

• Paper: https://arxiv.org/abs/2303.09101
• Code: https://github.com/Huang-ShiRui/Semi-UIR
• Tags: Underwater Image Restoration

Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior

• Paper: https://arxiv.org/abs/2303.15046
• Code: https://github.com/ykdai/BracketFlare
• Tags: Reflective Flare Removal

Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera

• Paper: https://arxiv.org/abs/2304.06019
• Code: https://github.com/jnjaby/AlignFormer

GamutMLP - A Lightweight MLP for Color Loss Recovery

• Paper: https://arxiv.org/abs/2304.11743
• Code: https://github.com/hminle/gamut-mlp
• Tags: restore wide-gamut color values

ABCD : Arbitrary Bitwise Coefficient for De-quantization

• Paper: https://ipl.dgist.ac.kr/ABCD_cvpr23.pdf
• Code: https://github.com/WooKyoungHan/ABCD
• Tags: De-quantization/Bit depth expansion

Image Reconstruction

Raw Image Reconstruction with Learned Compact Metadata

• Paper: https://arxiv.org/abs/2302.12995
• Code: https://github.com/wyf0912/R2LCM

High-resolution image reconstruction with latent diffusion models from human brain activity

• Paper: https://www.biorxiv.org/content/10.1101/2022.11.18.517004v2
• Code: https://github.com/yu-takagi/StableDiffusionReconstruction

Burst Restoration

Burstormer: Burst Image Restoration and Enhancement Transformer

• Paper: https://arxiv.org/abs/2304.01194
• Code: https://github.com/akshaydudhane16/Burstormer

Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement

• Paper: https://arxiv.org/abs/2304.06703

Video Restoration

Blind Video Deflickering by Neural Filtering with a Flawed Atlas

• Paper: https://arxiv.org/abs/2303.08120
• Code: https://github.com/ChenyangLEI/All-In-One-Deflicker
• Tags: Deflickering

Super Resolution - 超分辨率

Image Super Resolution

Activating More Pixels in Image Super-Resolution Transformer

• Paper: https://arxiv.org/abs/2205.04437
• Code: https://github.com/XPixelGroup/HAT
• Tags: Transformer

N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution

• Paper: https://arxiv.org/abs/2211.11436
• Code: https://github.com/rami0205/NGramSwin

Omni Aggregation Networks for Lightweight Image Super-Resolution

• Paper: https://arxiv.org/abs/2304.10244
• Code: https://github.com/Francis0625/Omni-SR

OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution

• Paper: https://arxiv.org/abs/2303.01091

Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

• Paper: https://arxiv.org/abs/2303.05156

Super-Resolution Neural Operator

• Paper: https://arxiv.org/abs/2303.02584
• Code: https://github.com/2y7c3/Super-Resolution-Neural-Operator

Human Guided Ground-truth Generation for Realistic Image Super-resolution

• Paper: https://arxiv.org/abs/2303.13069
• Code: https://github.com/ChrisDud0257/PosNegGT

Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution

• Paper: https://arxiv.org/abs/2304.03542
• Tags: Blind

Implicit Diffusion Models for Continuous Super-Resolution

• Paper: https://arxiv.org/abs/2303.16491
• Code: https://github.com/Ree1s/IDM

Zero-Shot Dual-Lens Super-Resolution

• Paper:
• Code: https://github.com/XrKang/ZeDuSR

CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input

• Paper: https://arxiv.org/abs/2304.06454

Learning Generative Structure Prior for Blind Text Image Super-resolution

• Paper: https://arxiv.org/abs/2303.14726
• Code: https://github.com/csxmli2016/MARCONet
• Tags: Text SR

Guided Depth Super-Resolution by Deep Anisotropic Diffusion

• Paper: https://arxiv.org/abs/2211.11592
• Code: https://github.com/prs-eth/Diffusion-Super-Resolution
• Tags: Guided Depth SR

Quantum Annealing for Single Image Super-Resolution

• Paper: https://arxiv.org/abs/2304.08924
• Tags: [Workshop]

Video Super Resolution

Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting

• Paper: https://arxiv.org/abs/2303.08331
• Code: https://github.com/coulsonlee/STDO-CVPR2023

Structured Sparsity Learning for Efficient Video Super-Resolution

• Paper: https://github.com/Zj-BinXia/SSL
• Code: https://arxiv.org/abs/2206.07687

Image Rescaling - 图像缩放

HyperThumbnail: Real-time 6K Image Rescaling with Rate-distortion Optimization

• Paper: https://arxiv.org/abs/2304.01064
• Code: https://github.com/AbnerVictor/HyperThumbnail

Denoising - 去噪

Image Denoising

Masked Image Training for Generalizable Deep Image Denoising

• Paper: https://arxiv.org/abs/2303.13132
• Code: https://github.com/haoyuc/MaskedDenoising

Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising

• Paper: https://arxiv.org/abs/2303.14934
• Cdoe: https://github.com/nagejacob/SpatiallyAdaptiveSSID
• Tags: Self-Supervised

LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising

• Paper: https://arxiv.org/abs/2304.00534
• Code: https://github.com/Wang-XIaoDingdd/LGBPN
• Tags: Self-Supervised

Real-time Controllable Denoising for Image and Video

• Paper: https://arxiv.org/pdf/2303.16425.pdf

Deblurring - 去模糊

Image Deblurring

Structured Kernel Estimation for Photon-Limited Deconvolution

• Paper: https://arxiv.org/abs/2303.03472
• Code: https://github.com/sanghviyashiitb/structured-kernel-cvpr23

Blur Interpolation Transformer for Real-World Motion from Blur

• Paper: https://arxiv.org/abs/2211.11423
• Code: https://github.com/zzh-tech/BiT

Neumann Network with Recursive Kernels for Single Image Defocus Deblurring

• Paper:
• Code: https://github.com/csZcWu/NRKNet

Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

• Paper: https://arxiv.org/abs/2211.12250
• Code: https://github.com/kkkls/FFTformer

Deraining - 去雨

Learning A Sparse Transformer Network for Effective Image Deraining

• Paper: https://arxiv.org/abs/2303.11950
• Code: https://github.com/cschenxiang/DRSformer

Dehazing - 去雾

RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors

• Paper: https://arxiv.org/abs/2304.03994
• Code: https://github.com/RQ-Wu/RIDCP

Curricular Contrastive Regularization for Physics-aware Single Image Dehazing

• Paper: https://arxiv.org/abs/2303.14218
• Code: https://github.com/YuZheng9/C2PNet

Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior

• Paper: https://arxiv.org/abs/2303.09757
• Code: https://github.com/jiaqixuac/MAP-Net

SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing

• Paper: https://arxiv.org/abs/2304.08444
• Code: https://github.com/gy65896/SCANet
• Tags: [Workshop]

HDR Imaging / Multi-Exposure Image Fusion - HDR图像生成 / 多曝光图像融合

Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models

• Paper: https://arxiv.org/abs/2303.13031
• Code: https://github.com/AndreGuo/HDRTVDM

SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders

• Paper: https://arxiv.org/abs/2304.06914

A Unified HDR Imaging Method with Pixel and Patch Level

• Paper: https://arxiv.org/abs/2304.06943

Inverting the Imaging Process by Learning an Implicit Camera Model

• Paper: https://arxiv.org/abs/2304.12748
• Code: https://github.com/xhuangcv/neucam
• Tags: generating all-in-focus photos & HDR imaging

Frame Interpolation - 插帧

Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation

• Paper: https://arxiv.org/abs/2303.00440
• Code: https://github.com/MCG-NJU/EMA-VFI

A Unified Pyramid Recurrent Network for Video Frame Interpolation

• Paper: https://arxiv.org/abs/2211.03456
• Code: https://github.com/srcn-ivl/UPR-Net

BiFormer: Learning Bilateral Motion Estimation via Bilateral Transformer for 4K Video Frame Interpolation

• Paper: https://arxiv.org/abs/2304.02225
• Code: https://github.com/JunHeum/BiFormer

AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation

• Paper: https://arxiv.org/abs/2304.09790
• Code: https://github.com/MCG-NKU/AMT

Event-based Video Frame Interpolation with Cross-Modal Asymmetric Bidirectional Motion Fields

• Paper:
• Code: https://github.com/intelpro/CBMNet
• Tags: Event-based

Event-based Blurry Frame Interpolation under Blind Exposure

• Paper:
• Code: https://github.com/WarranWeng/EBFI-BE
• Tags: Event-based

Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time

• Paper: https://arxiv.org/abs/2303.15043
• Code: https://github.com/shangwei5/VIDUE
• Tags: Frame Interpolation and Deblurring

Image Enhancement - 图像增强

Low-Light Image Enhancement

Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement

• Paper: https://arxiv.org/abs/2304.07039
• Code: https://github.com/langmanbusi/Semantic-Aware-Low-Light-Image-Enhancement

Visibility Constrained Wide-band Illumination Spectrum Design for Seeing-in-the-Dark

• Paper: https://arxiv.org/abs/2303.11642
• Code: https://github.com/MyNiuuu/VCSD
• Tags: NIR2RGB

Image Matting - 图像抠图

Referring Image Matting

• Paper: https://arxiv.org/abs/2206.05149
• Code: https://github.com/JizhiziLi/RIM

Adaptive Human Matting for Dynamic Videos

• Paper: https://arxiv.org/abs/2304.06018
• Code: https://github.com/microsoft/AdaM

Shadow Removal - 阴影消除

ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal

• Paper: https://arxiv.org/abs/2212.04711
• Code: https://github.com/GuoLanqing/ShadowDiffusion

Image Compression - 图像压缩

Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger

• Paper: https://arxiv.org/abs/2302.14677

Context-based Trit-Plane Coding for Progressive Image Compression

• Paper: https://arxiv.org/abs/2303.05715
• Code: https://github.com/seungminjeon-github/CTC

Learned Image Compression with Mixed Transformer-CNN Architectures

• Paper: https://arxiv.org/abs/2303.14978
• Code: https://github.com/jmliu206/LIC_TCM

Video Compression

Neural Video Compression with Diverse Contexts

• Paper: https://github.com/microsoft/DCVC
• Code: https://arxiv.org/abs/2302.14402

Image Quality Assessment - 图像质量评价

Quality-aware Pre-trained Models for Blind Image Quality Assessment

• Paper: https://arxiv.org/abs/2303.00521

Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective

• Paper: https://arxiv.org/abs/2303.14968
• Code: https://github.com/zwx8981/LIQE

Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method

• Paper: https://arxiv.org/abs/2303.15166
• Code: https://github.com/Dreemurr-T/BAID

Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild

• Paper: https://arxiv.org/abs/2304.00451

Style Transfer - 风格迁移

Fix the Noise: Disentangling Source Feature for Controllable Domain Translation

• Paper: https://arxiv.org/abs/2303.11545
• Code: https://github.com/LeeDongYeun/FixNoise

Neural Preset for Color Style Transfer

• Paper: https://arxiv.org/abs/2303.13511
• Code: https://github.com/ZHKKKe/NeuralPreset

CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer

• Paper: https://arxiv.org/abs/2303.17867

StyleGAN Salon: Multi-View Latent Optimization for Pose-Invariant Hairstyle Transfer

• Paper: https://arxiv.org/abs/2304.02744
• Project: https://stylegan-salon.github.io/

Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer

• Paper: https://arxiv.org/abs/2304.04461
• Project: https://kaist-viclab.github.io/old-photo-modernization/

QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity

• Paper: https://arxiv.org/abs/2212.10431
• Code: https://github.com/siyuhuang/QuantArt

Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer

• Paper: https://arxiv.org/abs/2304.11818

Image Editing - 图像编辑

Imagic: Text-Based Real Image Editing with Diffusion Models

• Paper: https://arxiv.org/abs/2210.09276

SINE: SINgle Image Editing with Text-to-Image Diffusion Models

• Paper: https://arxiv.org/abs/2212.04489
• Code: https://github.com/zhang-zx/SINE

CoralStyleCLIP: Co-optimized Region and Layer Selection for Image Editing

• Paper: https://arxiv.org/abs/2303.05031

DeltaEdit: Exploring Text-free Training for Text-Driven Image Manipulation

• Paper: https://arxiv.org/abs/2303.06285
• Code: https://arxiv.org/abs/2303.06285

SIEDOB: Semantic Image Editing by Disentangling Object and Background

• Paper: https://arxiv.org/abs/2303.13062
• Code: https://github.com/WuyangLuo/SIEDOB

DiffusionRig: Learning Personalized Priors for Facial Appearance Editing

• Paper: https://arxiv.org/abs/2304.06711
• Code: https://github.com/adobe-research/diffusion-rig

Image Generation/Synthesis / Image-to-Image Translation - 图像生成/合成/转换

Text-to-Image / Text Guided / Multi-Modal

Multi-Concept Customization of Text-to-Image Diffusion

• Paper: https://arxiv.org/abs/2212.04488
• Code: https://github.com/adobe-research/custom-diffusion

GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis

• Paper: https://arxiv.org/abs/2301.12959
• Code: https://github.com/tobran/GALIP

Scaling up GANs for Text-to-Image Synthesis

• Paper: https://arxiv.org/abs/2303.05511
• Project: https://mingukkang.github.io/GigaGAN/

MAGVLT: Masked Generative Vision-and-Language Transformer

• Paper: https://arxiv.org/abs/2303.12208

Freestyle Layout-to-Image Synthesis

• Paper: https://arxiv.org/abs/2303.14412
• Code: https://github.com/essunny310/FreestyleNet

Variational Distribution Learning for Unsupervised Text-to-Image Generation

• Paper: https://arxiv.org/abs/2303.16105

Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment

• Paper: https://arxiv.org/abs/2303.17490
• Project: https://sound2scene.github.io/

Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation

• Paper: https://arxiv.org/abs/2304.01816

Shifted Diffusion for Text-to-image Generation

• Paper: https://arxiv.org/abs/2211.15388
• Code: https://github.com/drboog/Shifted_Diffusion

Collaborative Diffusion for Multi-Modal Face Generation and Editing

• Paper: https://arxiv.org/abs/2304.10530
• Code: https://github.com/ziqihuangg/Collaborative-Diffusion

Image-to-Image / Image Guided

LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data

• Paper: https://arxiv.org/abs/2208.14889
• Code: https://github.com/KU-CVLAB/LANIT

Person Image Synthesis via Denoising Diffusion Model

• Paper: https://arxiv.org/abs/2211.12500
• Code: https://github.com/ankanbhunia/PIDM

Picture that Sketch: Photorealistic Image Generation from Abstract Sketches

• Paper: https://arxiv.org/abs/2303.11162

Fine-Grained Face Swapping via Regional GAN Inversion

• Paper: https://arxiv.org/abs/2211.14068
• Code: https://github.com/e4s2022/e4s

Masked and Adaptive Transformer for Exemplar Based Image Translation

• Paper: https://arxiv.org/abs/2303.17123
• Code: https://github.com/AiArt-HDU/MATEBIT

Zero-shot Generative Model Adaptation via Image-specific Prompt Learning

• Paper: https://arxiv.org/abs/2304.03119
• Code: https://github.com/Picsart-AI-Research/IPL-Zero-Shot-Generative-Model-Adaptation

Others for image generation

AdaptiveMix: Robust Feature Representation via Shrinking Feature Space

• Paper: https://arxiv.org/abs/2303.01559
• Code: https://github.com/WentianZhang-ML/AdaptiveMix

MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis

• Paper: https://arxiv.org/abs/2211.09117
• Code: https://github.com/LTH14/mage

Regularized Vector Quantization for Tokenized Image Synthesis

• Paper: https://arxiv.org/abs/2303.06424

Towards Accurate Image Coding: Improved Autoregressive Image Generation with Dynamic Vector Quantization

• Paper:
• Code: https://github.com/CrossmodalGroup/DynamicVectorQuantization

Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation

• Paper:
• Code: https://github.com/CrossmodalGroup/MaskedVectorQuantization

Exploring Incompatible Knowledge Transfer in Few-shot Image Generation

• Paper:
• Code: https://github.com/yunqing-me/RICK

Post-training Quantization on Diffusion Models

• Paper: https://arxiv.org/abs/2211.15736
• Code: https://github.com/42Shawn/PTQ4DM

LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation

• Paper: https://arxiv.org/abs/2303.17189
• Code: https://github.com/ZGCTroy/LayoutDiffusion

DiffCollage: Parallel Generation of Large Content with Diffusion Models

• Paper: https://arxiv.org/abs/2303.17076
• Project: https://research.nvidia.com/labs/dir/diffcollage/

Few-shot Semantic Image Synthesis with Class Affinity Transfer

• Paper: https://arxiv.org/abs/2304.02321

NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs

• Paper: https://arxiv.org/abs/2304.05866
• Code: https://github.com/val-iisc/NoisyTwins

DCFace: Synthetic Face Generation with Dual Condition Diffusion Model

• Paper: https://arxiv.org/abs/2304.07060
• Code: https://github.com/mk-minchul/dcface

Exploring Incompatible Knowledge Transfer in Few-shot Image Generation

• Paper: https://arxiv.org/abs/2304.07574
• Code: https://github.com/yunqing-me/RICK

Video Generation

Conditional Image-to-Video Generation with Latent Flow Diffusion Models

• Paper: https://arxiv.org/abs/2303.13744
• Code: https://github.com/nihaomiao/CVPR2023_LFDM

Video Probabilistic Diffusion Models in Projected Latent Space

• Paper: https://arxiv.org/abs/2302.07685
• Code: https://github.com/sihyun-yu/PVDM

DPE: Disentanglement of Pose and Expression for General Video Portrait Editing

• Paper: https://arxiv.org/abs/2301.06281
• Code: https://github.com/Carlyx/DPE

Decomposed Diffusion Models for High-Quality Video Generation

• Paper: https://arxiv.org/abs/2303.08320

Diffusion Video Autoencoders: Toward Temporally Consistent Face Video Editing via Disentangled Video Encoding

• Paper: https://arxiv.org/abs/2212.02802
• Code: https://github.com/man805/Diffusion-Video-Autoencoders

MoStGAN: Video Generation with Temporal Motion Styles

• Paper: https://arxiv.org/abs/2304.02777
• Code: https://github.com/xiaoqian-shen/MoStGAN

Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

• Paper: https://arxiv.org/abs/2304.08818

Others

Perspective Fields for Single Image Camera Calibration

• Paper: https://arxiv.org/abs/2212.03239
• Code: https://github.com/jinlinyi/PerspectiveFields

DC2: Dual-Camera Defocus Control by Learning to Refocus

• Paper: https://arxiv.org/abs/2304.03285
• Project: https://defocus-control.github.io/

Images Speak in Images: A Generalist Painter for In-Context Visual Learning

• Paper: https://arxiv.org/abs/2212.02499
• Code: https://github.com/baaivision/Painter

Make-A-Story: Visual Memory Conditioned Consistent Story Generation

• Paper: https://arxiv.org/abs/2211.13319
• Code: https://github.com/ubc-vision/Make-A-Story

Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models

• Paper: https://arxiv.org/abs/2303.10774
• Code: https://github.com/mattolson93/cross_gan_auditing

LightPainter: Interactive Portrait Relighting with Freehand Scribble

• Paper: https://arxiv.org/abs/2303.12950
• Tags: Portrait Relighting

Neural Texture Synthesis with Guided Correspondence

• Paper:
• Code: https://github.com/EliotChenKJ/Guided-Correspondence-Loss
• Tags: Texture Synthesis

Uncurated Image-Text Datasets: Shedding Light on Demographic Bias

• Paper: https://arxiv.org/abs/2304.02828
• Code: https://github.com/noagarcia/phase

Large-capacity and Flexible Video Steganography via Invertible Neural Network

• Paper: https://arxiv.org/abs/2304.12300
• Code: https://github.com/MC-E/LF-VSN
• Tags: Steganography

Talking Head Generation

Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert

• Paper: https://arxiv.org/abs/2303.17480
• Code: https://github.com/Sxjdwang/TalkLip

High-Fidelity and Freely Controllable Talking Head Video Generation

• Paper: https://arxiv.org/abs/2304.10168
• Code: https://github.com/hologerry/PECHead

MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation

• Paper: https://arxiv.org/abs/2212.08062
• Code: https://github.com/Meta-Portrait/MetaPortrait

Virtual Try-on

GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning

• Paper: https://arxiv.org/abs/2303.13756
• Code: https://github.com/xiezhy6/GP-VTON

Handwriting/Font Generation

CF-Font: Content Fusion for Few-shot Font Generation

• Paper: https://arxiv.org/abs/2303.14017
• Code: https://github.com/wangchi95/CF-Font
• Tags: Font Generation

DeepVecFont-v2: Exploiting Transformers to Synthesize Vector Fonts with Higher Quality

• Paper: https://arxiv.org/abs/2303.14585
• Code: https://github.com/yizhiwang96/deepvecfont-v2

Handwritten Text Generation from Visual Archetypes

• Paper: https://arxiv.org/abs/2303.15269
• Tags: Handwriting Generation

Disentangling Writer and Character Styles for Handwriting Generation

• Paper: https://arxiv.org/abs/2303.14736
• Code: https://github.com/dailenson/SDT
• Tags: Handwriting Generation

Layout Generation

Unifying Layout Generation with a Decoupled Diffusion Model

• Paper: https://arxiv.org/abs/2303.05049

Unsupervised Domain Adaption with Pixel-level Discriminator for Image-aware Layout Generation

• Paper: https://arxiv.org/abs/2303.14377

PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout

• Paper: https://arxiv.org/abs/2303.15937
• Code: https://github.com/PKU-ICST-MIPL/PosterLayout-CVPR2023

LayoutDM: Discrete Diffusion Model for Controllable Layout Generation

• Paper: https://arxiv.org/abs/2303.08137
• Code: https://github.com/CyberAgentAILab/layout-dm