mlsanityData to 7 Oct 2026

AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style

arXiv 2103.13722 ↗First posted 25 Mar 2021cs.CVcited by 11

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Abstract (arXiv)

Conditional image synthesis from layout has recently attracted much interest. Previous approaches condition the generator on object locations as well as class labels but lack fine-grained control over the diverse appearance aspects of individual objects. Gaining control over the image generation process is fundamental to build practical applications with a user-friendly interface. In this paper, we propose a method for attribute controlled image synthesis from layout which allows to specify the appearance of individual objects without affecting the rest of the image. We extend a state-of-the-art approach for layout-to-image generation to additionally condition individual objects on attributes. We create and experiment on a synthetic, as well as the challenging Visual Genome dataset. Our qualitative and quantitative results show that our method can successfully control the fine-grained details of individual objects when modelling complex scenes with multiple objects. Source code, dataset and pre-trained models are publicly available (https://github.com/stanifrolov/AttrLostGAN).

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 49 of its 53 references that are in the map. First posted on arXiv 25 Mar 2021.

Builds on

49 of 53 references.

References · 49

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37 more
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  21. 2017Semantic Image Synthesis via Adversarial Learningcited by 87
  22. 2019Image Synthesis From Reconfigurable Layout and Stylecited by 85
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 3; 2023: 2; 2024: 1; 2025: 1; 2026: 1.

11 papers.

  1. 2022ReCo: Region-Controlled Text-to-Image Generationcited by 177
  2. 2021Multimodal Image Synthesis and Editing: The Generative AI Eracited by 52
  3. 2022Modeling Image Composition for Complex Scene Generationcited by 43
  4. 2023Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generationcited by 13
  5. 2021Layered Controllable Video Generationcited by 7
  6. 2021Combining Transformer Generators with Convolutional Discriminatorscited by 5
  7. 2023Controllable Image Generation via Collage Representationscited by 5
  8. 2022DT2I: Dense Text-to-Image Generation from Region Descriptionscited by 4
  9. 2025Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generationcited by 2
  10. 2026BBQ-to-Image: Numeric Bounding Box and Qolor Control in Large-Scale Text-to-Image Modelscited by 2
  11. 2024ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-Image Generationcited by 1