CoCoIns: Consistent Subject Generation via Contrastive Instantiated Concepts
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915652440162304 |
|---|---|
| author | Hsin-Ying, Lee Chan, Kelvin C. K. Yang, Ming-Hsuan |
| author_facet | Hsin-Ying, Lee Chan, Kelvin C. K. Yang, Ming-Hsuan |
| contents | While text-to-image generative models can synthesize diverse and faithful content, subject variation across multiple generations limits their application to long-form content generation. Existing approaches require time-consuming fine-tuning, reference images for all subjects, or access to previously generated content. We introduce Contrastive Concept Instantiation (CoCoIns), a framework that effectively synthesizes consistent subjects across multiple independent generations. The framework consists of a generative model and a mapping network that transforms input latent codes into pseudo-words associated with specific concept instances. Users can generate consistent subjects by reusing the same latent codes. To construct such associations, we propose a contrastive learning approach that trains the network to distinguish between different combinations of prompts and latent codes. Extensive evaluations on human faces with a single subject show that CoCoIns performs comparably to existing methods while maintaining greater flexibility. We also demonstrate the potential for extending CoCoIns to multiple subjects and other object categories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_24387 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoCoIns: Consistent Subject Generation via Contrastive Instantiated Concepts Hsin-Ying, Lee Chan, Kelvin C. K. Yang, Ming-Hsuan Computer Vision and Pattern Recognition While text-to-image generative models can synthesize diverse and faithful content, subject variation across multiple generations limits their application to long-form content generation. Existing approaches require time-consuming fine-tuning, reference images for all subjects, or access to previously generated content. We introduce Contrastive Concept Instantiation (CoCoIns), a framework that effectively synthesizes consistent subjects across multiple independent generations. The framework consists of a generative model and a mapping network that transforms input latent codes into pseudo-words associated with specific concept instances. Users can generate consistent subjects by reusing the same latent codes. To construct such associations, we propose a contrastive learning approach that trains the network to distinguish between different combinations of prompts and latent codes. Extensive evaluations on human faces with a single subject show that CoCoIns performs comparably to existing methods while maintaining greater flexibility. We also demonstrate the potential for extending CoCoIns to multiple subjects and other object categories. |
| title | CoCoIns: Consistent Subject Generation via Contrastive Instantiated Concepts |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.24387 |