mlsanityData to 7 Oct 2026

iCONTRA: Toward Thematic Collection Design Via Interactive Concept Transfer

arXiv 2403.08746 ↗First posted 13 Mar 2024cs.CVcited by 3

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Abstract (arXiv)

Creating thematic collections in industries demands innovative designs and cohesive concepts. Designers may face challenges in maintaining thematic consistency when drawing inspiration from existing objects, landscapes, or artifacts. While AI-powered graphic design tools offer help, they often fail to generate cohesive sets based on specific thematic concepts. In response, we introduce iCONTRA, an interactive CONcept TRAnsfer system. With a user-friendly interface, iCONTRA enables both experienced designers and novices to effortlessly explore creative design concepts and efficiently generate thematic collections. We also propose a zero-shot image editing algorithm, eliminating the need for fine-tuning models, which gradually integrates information from initial objects, ensuring consistency in the generation process without influencing the background. A pilot study suggests iCONTRA's potential to reduce designers' efforts. Experimental results demonstrate its effectiveness in producing consistent and high-quality object concept transfers. iCONTRA stands as a promising tool for innovation and creative exploration in thematic collection design. The source code will be available at: https://github.com/vdkhoi20/iCONTRA.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 17 references that are in the map. First posted on arXiv 13 Mar 2024.

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17 of 17 references.

References · 17

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2023Segment Anythingcited by 8,158
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  5. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  6. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  7. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
  8. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,172
  9. 2023MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editingcited by 722
  10. 2023Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Modelscited by 696
  11. 2021DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulationcited by 640
  12. 2017Unsupervised Image-to-Image Translation Networkscited by 602
5 more
  1. 2023Zero-shot Image-to-Image Translationcited by 503
  2. 2017Neural Style Transfer: A Reviewcited by 199
  3. 2017Demystifying Neural Style Transfercited by 152
  4. 2022Direct Inversion: Optimization-Free Text-Driven Real Image Editing with Diffusion Modelscited by 19
  5. 2023VIDES: Virtual Interior Design via Natural Language and Visual Guidancecited by 1

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Citing papers per year: 2025: 1; 2026: 2.

3 papers.