Data to 7 Oct 2026

Learning What and Where to Draw

arXiv 1610.02454 ↗First posted 8 Oct 2016cs.CV cs.NEcited by 105

Not on the map. Why?

Cited by 25 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generative Adversarial Networks (GANs) have recently demonstrated the capability to synthesize compelling real-world images, such as room interiors, album covers, manga, faces, birds, and flowers. While existing models can synthesize images based on global constraints such as a class label or caption, they do not provide control over pose or object location. We propose a new model, the Generative Adversarial What-Where Network (GAWWN), that synthesizes images given instructions describing what content to draw in which location. We show high-quality 128 x 128 image synthesis on the Caltech-UCSD Birds dataset, conditioned on both informal text descriptions and also object location. Our system exposes control over both the bounding box around the bird and its constituent parts. By modeling the conditional distributions over part locations, our system also enables conditioning on arbitrary subsets of parts (e.g. only the beak and tail), yielding an efficient interface for picking part locations. We also show preliminary results on the more challenging domain of text- and location-controllable synthesis of images of human actions on the MPII Human Pose dataset.

Key facts. Cited by 105 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Oct 2016.

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Citing papers per year: 2021: 28; 2022: 25; 2023: 32; 2024: 15; 2025: 5.

105 papers.

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2021Zero-Shot Text-to-Image Generationcited by 4,276
2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
2023Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generatorscited by 666
2023Scaling up GANs for Text-to-Image Synthesiscited by 492
2023InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generationcited by 387
2023LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generationcited by 258
2023T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generationcited by 253
2022SpaText: Spatio-Textual Representation for Controllable Image Generationcited by 217
2023StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesiscited by 212
2021More Control for Free! Image Synthesis with Semantic Diffusion Guidancecited by 211
2022Optimizing Prompts for Text-to-Image Generationcited by 204
2021Illiterate DALL-E Learns to Composecited by 147

38 more

2023Localizing Object-level Shape Variations with Text-to-Image Diffusion Modelscited by 142
2023Collaborative Diffusion for Multi-Modal Face Generation and Editingcited by 116
2023CoDeF: Content Deformation Fields for Temporally Consistent Video Processingcited by 108
2023LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generationcited by 92
2024Distilling Diffusion Models into Conditional GANscited by 78
2021Learning to Compose Visual Relationscited by 72
2022Towards Implicit Text-Guided 3D Shape Generationcited by 68
2023Freestyle Layout-to-Image Synthesiscited by 67
2023Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Modelscited by 67
2022Synthetic Data -- what, why and how?cited by 66
2024CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Modelcited by 54
2023Visual Programming for Text-to-Image Generation and Evaluationcited by 53
2021Adversarial Text-to-Image Synthesis: A Reviewcited by 52
2021Multimodal Image Synthesis and Editing: The Generative AI Eracited by 52
2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
2024Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?cited by 52
2023RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Modelcited by 47
2022SceneComposer: Any-Level Semantic Image Synthesiscited by 43
2023If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selectioncited by 40
2023High-Fidelity 3D Face Generation from Natural Language Descriptionscited by 32
2022VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programmingcited by 31
2022Attention Mechanism in Neural Networks: Where it Comes and Where it Goescited by 29
2022Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networkscited by 28
2023RSDiff: Remote Sensing Image Generation from Text Using Diffusion Modelcited by 28
2023ControlNet-XS: Rethinking the Control of Text-to-Image Diffusion Models as Feedback-Control Systemscited by 28
2022ISS: Image as Stepping Stone for Text-Guided 3D Shape Generationcited by 27
2023Multi-modal Machine Learning in Engineering Design: A Review and Future Directionscited by 27
2023AI-Generated Content (AIGC) for Various Data Modalities: A Surveycited by 25
2023Graph Transformer GANs for Graph-Constrained House Generationcited by 19
2023Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignmentcited by 18
2022A survey on GANs for computer vision: Recent research, analysis and taxonomycited by 15
2022Testing Human Ability To Detect Deepfake Images of Human Facescited by 15
2022CoGS: Controllable Generation and Search from Sketch and Stylecited by 14
2023T2TD: Text-3D Generation Model based on Prior Knowledge Guidancecited by 13
2024Text2Street: Controllable Text-to-image Generation for Street Viewscited by 13
2024Geospecific View Generation -- Geometry-Context Aware High-resolution Ground View Inference from Satellite Viewscited by 13
2022Recurrent Affine Transformation for Text-to-image Synthesiscited by 11
2022A survey on knowledge-enhanced multimodal learningcited by 11

50 most cited of 105 All 105 (JSON)