Schedule

Reading List:

• Szeliski Book, Chapters 3.1, 3.2, and 3.5
• Color Transfer between Images
• Non-rigid dense correspondence with applications for image enhancement

Reading list:

• Szeliski Book, Chapter 3.6
• Image Deformation using Moving Least Squares, Schaefer et al. in TOG, 2006

Reading list:

• Poisson Image Editing, Pérez et al. in SIGGRAPH, 2003
• Scene Completion using Millions of Photographs, Hays et al. in TOG, 2007
• CG2Real: Improving the Realism of Computer Generated Images using a Large Collection of Photographs, Johnson et al. in TVCG, 2010
• Modeling the shape of the scene: A holistic representation of the spatial envelope, Oliva et al. in IJCV, 2001
• Semantic photo synthesis, Johnson et al. in Computer Graphics Forum, 2006
• Sketch2Photo: internet image montage, Chen et al. in SIGGRAPH, 2019
• Photo Clip Art, Lalonde et al. in SIGGRAPH, 2007
• ShadowDraw: Real-Time User Guidance for Freehand Drawing, Lee et al. in SIGGRAPH, 2011
• AverageExplorer: Interactive Exploration and Alignment of Visual Data Collections, Zhu et al. in SIGGRAPH, 2014
• Image Deformation Using Moving Least Squares, Schaefer et al. in SIGGRAPH, 2006

Reading list:

• Deep Learning Book, Chapter 6 and 9.
• Szeliski Book, Chapter 5.3 and 5.4.
• Gradient-based learning applied to document recognition, Lecun et al., Proc of IEEE, 1998.
• Receptive Fields of Single Neurones in the Cat’s Striate Cortex, Huber and Wiesel, J. Physiol, 1959.
• Learning to Generate Chairs, Tables and Cars with Convolutional Networks, Dosovitskiy et al., PAMI 2017 (CVPR 2015)
• Deconvolution and Checkerboard Artifacts
• Colorful Image Colorization, Zhang et al., ECCV 2016.
• Deep Learning, LeCun, Bengio, and Hinton, Nature 2015.
• Notes on Backpropagation and CNNs: Olah and 231n

Reading List:

• Szeliski Book, Chapter 5.5.3
• Generative Adversarial Networks, Goodfellow et al., NeurIPS 2014.
• GAN Tutorial (NeurIPS 2016), by Ian Goodfellow.
• ICCV 2017 Tutorial on GANs.
• CVPR 2018 Tutorial on GANs.
• Image style transfer using convolutional neural networks, Gatys et al., CVPR 2016.
• Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Johnson et al., ECCV 2016.
• Generating images with perceptual similarity metrics based on deep networks. Dosovitskiy and Brox. NeurIPS, 2016
• The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Zhang et al., CVPR 2018

Reading List:

• Szeliski Book, Chapter 5.5.3
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Radford et al., ICLR 2016
• Wasserstein GAN, Arjovsky et al, 2017
• Improved Training of Wasserstein GANs, Gulrajani et al., NeurIPS 2017
• Least Squares Generative Adversarial Networks, Mao et al. 2017
• Progressive Growing of GANs for Improved Quality, Stability, and Variation, Karras et al, ICLR 2018
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, Denton et al, 2015
• Spectral Normalization for Generative Adversarial Networks, Miyato et al., ICLR 2018
• A Style-Based Generator Architecture for Generative Adversarial Networks, Karras et al., CVPR 2019
• Analyzing and Improving the Image Quality of StyleGAN, Karras et al., 2020
• Alias-Free Generative Adversarial Networks, Karras et al., 2021
• Training Generative Adversarial Networks with Limited Data, Karras et al., 2020
• Differentiable Augmentation for Data-Efficient GAN Training, Zhao et al., 2020
• Image Manipulation with Perceptual Discriminators, Sungatullina et al., ECCV 2018.
• Projected GANs Converge Faster, Sauer et al., 2021
• Ensembling Off-the-shelf Models for GAN Training, Kumari et al., 2021

Reading List:

• Deep Learning Book, Chapter 20
• Learning Deep Generative Models, Ruslan Salakhutdinov, Annual Review of Applied Statistics 2015
• Auto-Encoding Variational Bayes, Kingma and Welling, 2013
• Conditional Image Generation with PixelCNN Decoders, Oord et al, 2016
• Pixel Recurrent Neural Networks, Oord et al, 2016
• Autoencoding beyond pixels using a learned similarity metric, Larsen et al, CVPR 2016
• Generating Diverse High-Fidelity Images with VQ-VAE-2, Razavi et al., 2019

Reading List:

• Deep Learning Book, Chapters 14 and 20
• Density estimation using Real NVP, Dinh et al, ICLR 2017
• Glow: Generative flow with invertible 1x1 convolutions, Kingma et al., 2018
• Denoising Diffusion Probabilistic Models, Ho et al., 2020
• Denoising Diffusion Implicit Models, Song et al., 2021
• Score-Based Generative Modeling through Stochastic Differential Equations (SDE), Song et al., ICLR 2021
• High-Resolution Image Synthesis with Latent Diffusion Models, Rombach et al., CVPR 2022.

Reading List:

• Alias-Free Generative Adversarial Networks (StyleGAN3), Karras et al., 2021
• High-Resolution Image Synthesis with Latent Diffusion Models (LDM), Rombach et al., CVPR 2022.
• Taming Transformers for High-Resolution Image Synthesis (VQGAN), Esser et al., 2020
• Glow: Generative Flow with Invertible 1x1 Convolutions (Glow), Kingma and Dhariwal, 2018
• Denoising Diffusion Probabilistic Models (DDPM), Ho et al., 2020
• MaskGIT: Masked Generative Image Transformer (MaskGIT), Chang et al., 2022
• Conditional Image Generation with PixelCNN Decoders (PixelCNN), van den Oord et al., 2016

Reading List:

• Image-to-Image Translation with Conditional Adversarial Networks, Isola et al, CVPR 2017
• Photographic Image Synthesis with Cascaded Refinement Networks, Chen et al, ICCV 2017
• Toward Multimodal Image-to-Image Translation, Zhu et al, NeurIPS 2017
• Multi-Agent Diverse Generative Adversarial Networks, Ghosh et al, CVPR 2018
• Diversity-Sensitive Conditional Generative Adversarial Networks, Yang et al, ICLR 2019
• Large Scale GAN Training for High Fidelity Natural Image Synthesis, ICLR 2019

Reading List:

• High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs, Wang et al, CVPR 2018.
• Semantic Image Synthesis with Spatially-Adaptive Normalization, Park et al, CVPR 2019
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, Zhu et al, ICCV 2017
• Unsupervised Image-to-Image Translation Networks, Liu et al., NeurIPS 2017
• Learning from Simulated and Unsupervised Images through Adversarial Training, Shrivastava et al, CVPR 2017
• Unsupervised Cross-Domain Image Generation, Taigman et al, ICLR 2017
• Multimodal Unsupervised Image-to-Image Translation, Huang et al., ECCV 2018
• Representation learning with contrastive predictive coding, Oord et al., arXiv 2018.
• Contrastive learning for unpaired image-to-image translation, Park et al., ECCV 2020
• A Simple Framework for Contrastive Learning of Visual Representations, Chen et al, ICML 2020
• SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations, Meng et al., ICLR 2022

Reading List:

• Separating Style and Content, Tenenbaum & Freeman, Neurips 1996
• Swapping Autoencoder for Deep Image Manipulation, Park et al., NeurIPS 2020.
• Texture Synthesis by Non-parametric Sampling, Efros and Leung, ICCV 1999
• Image Quilting for Texture Synthesis and Transfer, Efros and Freeman, SIGGRAPH 2001.
• Image Analogies, Hertzmann et al, SIGGRAPH 2001
• PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing, Barnes et al, SIGGRAPH 2009
• Deep Photo Style Transfer, Luan et al., CVPR 2017
• Non-Stationary Texture Synthesis by Adversarial Expansion, Zhou et al, SIGGRAPH 2018
• Deep Image Harmonization, Luan et al., EGSR 2018

Reading List:

• A text-to-picture synthesis system for augmenting communication, Zhu et al., AAAI 2007
• Generating Images from Captions with Attention, Mansimov et al., ICLR 2016
• Generative Adversarial Text to Image Synthesis. Reed et al., ICML 2016
• StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. Zhang et al., ICCV 2017
• High-Resolution Image Synthesis with Latent Diffusion Models, Rombach et al., CVPR 2022
• Learning Transferable Visual Models From Natural Language Supervision, Radford et al., ICML 2021
• Hierarchical Text-Conditional Image Generation with CLIP Latents, Ramesh et al., arXiv 2022
• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. Saharia, Chan, et al., NeurIPS 2022
• Scaling Autoregressive Models for Content-Rich Text-to-Image Generation, Yu et al., TMLR 2022
• StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis. Sauer et al., 2022
• Scaling up GANs for Text-to-Image Synthesis, Kang et al., 2023

Reading List:

• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. Saharia, Chan, et al., NeurIPS 2022
• Scene-Based Text-to-Image Generation with Human Priors (Make-a-Scene), GAFNI ET AL., ARXIV 2022
• Scaling Autoregressive Models for Content-Rich Text-to-Image Generation, Yu et al., TMLR 2022
• Muse: Text-To-Image Generation via Masked Generative Transformers, Chang et al., arxiv 2023
• Zero-Shot Text-to-Image Generation (DALL·E), Ramesh et al., arxiv 2021
• Hierarchical Text-Conditional Image Generation with CLIP Latents, Ramesh et al., arXiv 2022

Reading List:

• Generative Visual Manipulation on the Natural Image Manifold, Zhu et al, ECCV 2016
• Neural Photo Editing with Introspective Adversarial Networks, Brock et al, ICLR 2017
• Semantic Photo Manipulation with a Generative Image Prior, Bau et al, SIGGRAPH 2019
• Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?, Abdal et al, ICCV 2019
• GANSpace: Discovering Interpretable GAN Controls, Härkönen et al, NeurIPS 2020
• StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation, Wu et al., CVPR 2021
• Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation, Pan et al., ECCV 2020
• On the “steerability” of generative adversarial networks, Jahanian et al, ICLR 2020
• Pivotal Tuning for Latent-based Editing of Real Images, Roich et al., 2021
• GAN Inversion: A Survey. Xia et al. 2021

Reading List:

• Denoising Diffusion Implicit Models, Song et al, ICLR 2021
• SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations, Meng et al, 2021
• ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models, Choi et al, ICCV 2021
• Prompt-to-Prompt Image Editing with Cross-Attention Control, Hertz et al, 2022
• An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion, Gal et al, 2022
• DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation, Ruiz et al., CVPR 2023
• Multi-Concept Customization of Text-to-Image Diffusion, Kumari et al., ECCV 2022

Reading List:

• Toward Accurate and Realistic Outfits Visualization with Attention to Details, Li et al., CVPR 2021
• InstructPix2Pix Learning to Follow Image Editing Instructions, Brooks et al., 2022.
• StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators, Gal et al., 2021
• StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery, Patashnik et al., 2021
• Colorization Using Optimization, Levin et al., 2004

Reading List:

• Manipulating Facial Appearance through Shape and Color, Rowland & Perret, CG&A, 1995
• A Morphable Model For The Synthesis Of 3D Faces, Blanz & Vetter, SIGGRAPH 1999
• Exploring Photobios, Kemelmacher-Shlizerman et al., SIGGRAPH 2011
• Deep Video Portraits, Kim et al, SIGGRAPH 2018
• GANimation: Anatomically-aware Facial Animation from a Single Image, Pumarola et al, ECCV 2018
• StyleRig: Rigging StyleGAN for 3D Control over Portrait Images, Tewari et al, CVPR 2020

Reading List:

• QuickTime VR – An Image-Based Approach to Virtual Environment Navigation, Chen, SIGGRAPH 1995
• Stanford Lightfield Website, 2004-2014
• View Synthesis by Appearance Flow, Zhou et al, ECCV 2016
• Single-view view synthesis with multiplane images, Tucker et al, CVPR 2020
• NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, Mildenhall et al, ECCV 2020

Reading List:

• Time-Travel Rephotography, Luo et al., 2021
• Deep Image Prior, Ulyanov et al., 2017.
• Imagic: Text-Based Real Image Editing with Diffusion Models, Kawar et al., 2022
• GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models, Nichol et al., 2021
• Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation, Lu et al., 2021

Reading List:

• GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis, Schwarz et al, NeurIPS 2020
• pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis, Chan et al, CVPR 2021
• Editing Conditional Radiance Fields, Liu et al, ICCV 2021
• EG3D: Efficient Geometry-aware 3D Generative Adversarial Networks, Chan et al, arXiv 2021
• StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis, Gu et al, arXiv 2021
• DreamFusion: Text-to-3D using 2D Diffusion, Ben Poole et al., ICLR 2023
• Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions, Haque et al., arXiv 2023

Reading List:

• Video Textures, Sch¨odl et al., SIGGRAPH 2000
• Controlled Animation of Video Sprites, Schodl et al., UIST 2008
• Generating Videos with Scene Dynamics, Vondrick et al., NeurIPS 2016
• Video-to-Video Synthesis, Wang et al., NeurIPS 2016
• Everybody Dance Now, Chan et al., ICCV 2019
• Few-shot Video-to-Video Synthesis, Wang et al., NeurIPS 2019
• Imagen Video: High Definition Video Generation with Diffusion Models, Ho et al., 2022

Reading List:

• Image Forensics. Computer Vision: A Reference Guide, Hany Farid, 2020
• Detecting Photoshopped Faces by Scripting Photoshop, Wang et al., ICCV 2019
• Faceforensics++: Learning to detect manipulated facial images, Rossler et al., ICCV 2019
• CNN-generated images are surprisingly easy to spot… for now, Wang et al., CVPR 2020.
• Extracting Training Data from Diffusion Models, Carlini et al., 2023
• Ablating Concepts in Text-to-Image Diffusion Models, Kumari et al., 2023

Reading List:

• BANMo: Building Animatable 3D Neural Models from Many Casual Videos
• Unsupervised Learning of Depth and Ego-Motion from Video
• Make-A-Video: Text-to-Video Generation without Text-Video Data
• Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
• Nerfies: Deformable Neural Radiance Fields
• Nerf in the wild: Neural radiance fields for unconstrained photo collections
• Unsupervised 3D Neural Rendering of Minecraft Worlds
• Background Matting: The World Is Your Green Screen