SIGMA-GEN: Structure and Identity Guided Multi-subject Assembly for Image Generation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909830893010944 |
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| author | Saha, Oindrila Krs, Vojtech Mech, Radomir Maji, Subhransu Blackburn-Matzen, Kevin Gadelha, Matheus |
| author_facet | Saha, Oindrila Krs, Vojtech Mech, Radomir Maji, Subhransu Blackburn-Matzen, Kevin Gadelha, Matheus |
| contents | We present SIGMA-GEN, a unified framework for multi-identity preserving image generation. Unlike prior approaches, SIGMA-GEN is the first to enable single-pass multi-subject identity-preserved generation guided by both structural and spatial constraints. A key strength of our method is its ability to support user guidance at various levels of precision -- from coarse 2D or 3D boxes to pixel-level segmentations and depth -- with a single model. To enable this, we introduce SIGMA-SET27K, a novel synthetic dataset that provides identity, structure, and spatial information for over 100k unique subjects across 27k images. Through extensive evaluation we demonstrate that SIGMA-GEN achieves state-of-the-art performance in identity preservation, image generation quality, and speed. Code and visualizations at https://oindrilasaha.github.io/SIGMA-Gen/ |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_06469 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SIGMA-GEN: Structure and Identity Guided Multi-subject Assembly for Image Generation Saha, Oindrila Krs, Vojtech Mech, Radomir Maji, Subhransu Blackburn-Matzen, Kevin Gadelha, Matheus Computer Vision and Pattern Recognition We present SIGMA-GEN, a unified framework for multi-identity preserving image generation. Unlike prior approaches, SIGMA-GEN is the first to enable single-pass multi-subject identity-preserved generation guided by both structural and spatial constraints. A key strength of our method is its ability to support user guidance at various levels of precision -- from coarse 2D or 3D boxes to pixel-level segmentations and depth -- with a single model. To enable this, we introduce SIGMA-SET27K, a novel synthetic dataset that provides identity, structure, and spatial information for over 100k unique subjects across 27k images. Through extensive evaluation we demonstrate that SIGMA-GEN achieves state-of-the-art performance in identity preservation, image generation quality, and speed. Code and visualizations at https://oindrilasaha.github.io/SIGMA-Gen/ |
| title | SIGMA-GEN: Structure and Identity Guided Multi-subject Assembly for Image Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.06469 |