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arXiv · 2605.23178

Composing People Together: Iterative Pose-Image Generation for Multi-Person Interaction Scenes

Wenxuan Peng·Bharath Hariharan·Hadar Averbuch-Elor
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Abstract

Despite recent progress, text-to-image models still struggle to generate semantically diverse and compositionally accurate multi-person interaction scenes, often collapsing to repetitive layouts, stereotypical poses, and poorly grounded interactions. In this work, we bridge this gap by introducing a dual pose-image representation that brings person-centric structural priors into pretrained diffusion transformers. Our model jointly predicts a 2D pose visualization image and its corresponding RGB image, enabling structure and appearance to co-evolve during learning. At its core, a cross-modal alignment scheme binds text, pose, and image representations, ensuring consistent grounding across modalities. Furthermore, we design an iterative scene construction scheme, progressively generating complex multi-human interactions while effectively decomposing the overall generation complexity. Extensive experiments demonstrate that our method substantially improves prompt alignment and scene diversity in multi-person image generation.

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Wenxuan Peng, Bharath Hariharan, Hadar Averbuch-Elor. 2026-05-22. Composing People Together: Iterative Pose-Image Generation for Multi-Person Interaction Scenes. https://doi.org/10.1145/3799902.3811129

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