“‘Video Generation’ Tag”,2020-01-15 (anime AI, diffusion model, StyleGAN, self-attention, GPT, AI scaling, model-based RL; backlinks)⁠:

Bibliography for tag ai/video/generation, most recent first: 71 annotations & 45 links (parent).

• See Also
• Links
  • “Diffusion Forcing: Next-Token Prediction Meets Full-Sequence Diffusion”, Chen et al 2024
  • “SF-V: Single Forward Video Generation Model”, Zhang et al 2024
  • “Sakuga-42M Dataset: Scaling Up Cartoon Research”, Pan et al 2024
  • “VideoGigaGAN: Towards Detail-Rich Video Super-Resolution”, Xu et al 2024
  • “Dynamic Typography: Bringing Text to Life via Video Diffusion Prior”, Liu et al 2024
  • “VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time”, Xu et al 2024
  • “CMD: Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition”, Yu et al 2024
  • “ZigMa: Zigzag Mamba Diffusion Model”, Hu et al 2024
  • “TF-T2V: A Recipe for Scaling up Text-To-Video Generation With Text-Free Videos”, Wang et al 2023
  • “W.A.L.T: Photorealistic Video Generation With Diffusion Models”, Gupta et al 2023
  • “StyleCrafter: Enhancing Stylized Text-To-Video Generation With Style Adapter”, Liu et al 2023
  • “MicroCinema: A Divide-And-Conquer Approach for Text-To-Video Generation”, Wang et al 2023
  • “I2VGen-XL: High-Quality Image-To-Video Synthesis via Cascaded Diffusion Models”, Zhang et al 2023
  • “Where Memory Ends and Generative AI Begins: New Photo Manipulation Tools from Google and Adobe Are Blurring the Lines between Real Memories and Those Dreamed up by AI”, Goode2023
  • “Parsing-Conditioned Anime Translation: A New Dataset and Method”, Li et al 2023c
  • “Dreamix: Video Diffusion Models Are General Video Editors”, Molad et al 2023
  • “OpenAI CEO Sam Altman on GPT-4: ‘People Are Begging to Be Disappointed and They Will Be’”, Vincent2023
  • “Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-To-Video Generation”, Wu et al 2022
  • “MAGVIT: Masked Generative Video Transformer”, Yu et al 2022
  • “Latent Video Diffusion Models for High-Fidelity Video Generation With Arbitrary Lengths”, He et al 2022
  • “AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies”, Siyao et al 2022
  • “Imagen Video: High Definition Video Generation With Diffusion Models”, Ho et al 2022
  • “Phenaki: Variable Length Video Generation From Open Domain Textual Description”, Villegas et al 2022
  • “Make-A-Video: Text-To-Video Generation without Text-Video Data”, Singer et al 2022
  • “CelebV-HQ: A Large-Scale Video Facial Attributes Dataset”, Zhu et al 2022
  • “InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images”, Li et al 2022
  • “NUWA-∞: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis”, Wu et al 2022
  • “OmniMAE: Single Model Masked Pretraining on Images and Videos”, Girdhar et al 2022
  • “Cascaded Video Generation for Videos In-The-Wild”, Castrejon et al 2022
  • “CogVideo: Large-Scale Pretraining for Text-To-Video Generation via Transformers”, Hong et al 2022
  • “Flexible Diffusion Modeling of Long Videos”, Harvey et al 2022
  • “Ethan Caballero on Private Scaling Progress”, Caballero & Trazzi2022
  • “Video Diffusion Models”, Ho et al 2022
  • “TATS: Long Video Generation With Time-Agnostic VQGAN and Time-Sensitive Transformer”, Ge et al 2022
  • “Reinforcement Learning With Action-Free Pre-Training from Videos”, Seo et al 2022
  • “Transframer: Arbitrary Frame Prediction With Generative Models”, Nash et al 2022
  • “Diffusion Probabilistic Modeling for Video Generation”, Yang et al 2022
  • “General-Purpose, Long-Context Autoregressive Modeling With Perceiver AR”, Hawthorne et al 2022
  • “Microdosing: Knowledge Distillation for GAN Based Compression”, Helminger et al 2022
  • “StyleGAN-V: A Continuous Video Generator With the Price, Image Quality and Perks of StyleGAN-2”, Skorokhodov et al 2021
  • “U.S. vs. China Rivalry Boosts Tech—And Tensions: Militarized AI Threatens a New Arms Race”, Smith2021
  • “NÜWA: Visual Synthesis Pre-Training for Neural VisUal World CreAtion”, Wu et al 2021
  • “Advances in Neural Rendering”, Tewari et al 2021
  • “Learning a Perceptual Manifold With Deep Features for Animation Video Resequencing”, Morace et al 2021
  • “Autoregressive Latent Video Prediction With High-Fidelity Image Generator”, Seo et al 2021
  • “FitVid: Overfitting in Pixel-Level Video Prediction”, Babaeizadeh et al 2021
  • “Alias-Free Generative Adversarial Networks”, Karras et al 2021
  • “GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation (works for Videos Too!)”, Chong & Forsyth2021
  • “NWT: Towards Natural Audio-To-Video Generation With Representation Learning”, Mama et al 2021
  • “Vector Quantized Models for Planning”, Ozair et al 2021
  • “GODIVA: Generating Open-DomaIn Videos from NAtural Descriptions”, Wu et al 2021
  • “VideoGPT: Video Generation Using VQ-VAE and Transformers”, Yan et al 2021
  • “China’s GPT-3? BAAI Introduces Superscale Intelligence Model ‘Wu Dao 1.0’: The Beijing Academy of Artificial Intelligence (BAAI) Releases Wu Dao 1.0, China’s First Large-Scale Pretraining Model.”, Synced2021
  • “Greedy Hierarchical Variational Autoencoders (GHVAEs) for Large-Scale Video Prediction”, Wu et al 2021
  • “CW-VAE: Clockwork Variational Autoencoders”, Saxena et al 2021
  • “Scaling Laws for Autoregressive Generative Modeling”, Henighan et al 2020
  • “SIREN: Implicit Neural Representations With Periodic Activation Functions”, Sitzmann et al 2020
  • “NeRF: Representing Scenes As Neural Radiance Fields for View Synthesis”, Mildenhall et al 2020
  • “High Fidelity Video Prediction With Large Stochastic Recurrent Neural Networks”, Villegas et al 2019
  • “Learning to Predict Without Looking Ahead: World Models Without Forward Prediction”, Freeman et al 2019
  • “Learning to Predict Without Looking Ahead: World Models Without Forward Prediction [Blog]”, Freeman et al 2019
  • “Scaling Autoregressive Video Models”, Weissenborn et al 2019
  • “NoGAN: Decrappification, DeOldification, and Super Resolution”, Antic et al 2019
  • “Model-Based Reinforcement Learning for Atari”, Kaiser et al 2019
  • “Parallel Multiscale Autoregressive Density Estimation”, Reed et al 2017
  • “VPN: Video Pixel Networks”, Kalchbrenner et al 2016
  • “THUDM/CogVideo: Text-To-Video Generation. The Repo for ICLR2023 Paper “CogVideo: Large-Scale Pretraining for Text-To-Video Generation via Transformers””
  • “PaintsUndo: A Base Model of Drawing Behaviors in Digital Paintings”
  • “Flexible Diffusion Modeling of Long Videos”
  • “Text2Bricks: Fine-Tuning Open-Sora in 1,000 GPU-Hours”
  • “EfficientZero: How It Works”
• Miscellaneous
• Bibliography

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