Ar2Can: An Architect and an Artist Leveraging a Canvas for Multi-Human Generation
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_ | 1866912993005010944 |
|---|---|
| author | Borse, Shubhankar Pham, Phuc Farhadzadeh, Farzad Choi, Seokeon Nguyen, Phong Ha Tran, Anh Tuan Yun, Sungrack Hayat, Munawar Porikli, Fatih |
| author_facet | Borse, Shubhankar Pham, Phuc Farhadzadeh, Farzad Choi, Seokeon Nguyen, Phong Ha Tran, Anh Tuan Yun, Sungrack Hayat, Munawar Porikli, Fatih |
| contents | Despite recent advances in personalized image generation, existing models consistently fail to produce reliable multi-human scenes, often merging or losing facial identity. We present Ar2Can, a novel two-stage framework that disentangles spatial planning from identity rendering for multi-human generation. The Architect predicts structured layouts, specifying where each person should appear. The Artist then synthesizes photorealistic images, guided by a spatially-grounded face matching reward that combines Hungarian spatial alignment with identity similarity. This approach ensures faces are rendered at correct locations and faithfully preserve reference identities. We develop two Architect variants, seamlessly integrated with our diffusion-based Artist model. This is optimized via Group Relative Policy Optimization (GRPO) using compositional rewards for count accuracy, image quality, and identity matching. Evaluated on the MultiHuman-Testbench, Ar2Can achieves substantial improvements in both count accuracy and identity preservation, while maintaining high perceptual quality. Notably, our method achieves these results using primarily synthetic data, without requiring real multi-human images. Project page: https://qualcomm-ai-research.github.io/ar2can/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22690 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ar2Can: An Architect and an Artist Leveraging a Canvas for Multi-Human Generation Borse, Shubhankar Pham, Phuc Farhadzadeh, Farzad Choi, Seokeon Nguyen, Phong Ha Tran, Anh Tuan Yun, Sungrack Hayat, Munawar Porikli, Fatih Computer Vision and Pattern Recognition Despite recent advances in personalized image generation, existing models consistently fail to produce reliable multi-human scenes, often merging or losing facial identity. We present Ar2Can, a novel two-stage framework that disentangles spatial planning from identity rendering for multi-human generation. The Architect predicts structured layouts, specifying where each person should appear. The Artist then synthesizes photorealistic images, guided by a spatially-grounded face matching reward that combines Hungarian spatial alignment with identity similarity. This approach ensures faces are rendered at correct locations and faithfully preserve reference identities. We develop two Architect variants, seamlessly integrated with our diffusion-based Artist model. This is optimized via Group Relative Policy Optimization (GRPO) using compositional rewards for count accuracy, image quality, and identity matching. Evaluated on the MultiHuman-Testbench, Ar2Can achieves substantial improvements in both count accuracy and identity preservation, while maintaining high perceptual quality. Notably, our method achieves these results using primarily synthetic data, without requiring real multi-human images. Project page: https://qualcomm-ai-research.github.io/ar2can/. |
| title | Ar2Can: An Architect and an Artist Leveraging a Canvas for Multi-Human Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.22690 |