Abstract
Person re-identification (ReID) suffers from a lack of large-scale high-quality training data due to challenges in data privacy and annotation costs. While previous approaches have explored pedestrian generation for data augmentation, they often fail to ensure identity consistency and suffer from insufficient controllability, thereby limiting their effectiveness in dataset augmentation. To address this, We introduce OmniPerson, the first unified identity-preserving pedestrian generation pipeline for visible/infrared image/video ReID tasks. Our contributions are threefold: 1) We proposed OmniPerson, a unified generation model, offering holistic and fine-grained control over all key pedestrian attributes. Supporting RGB/IR modality image/video generation with any number of reference images, two kinds of person poses, and text. Also including RGB-to-IR transfer and image super-resolution abilities.2) We designed Multi-Refer Fuser for robust identity preservation with any number of reference images as input, making OmniPerson could distill a unified identity from a set of multi-view reference images, ensuring our generated pedestrians achieve high-fidelity pedestrian generation.3) We introduce PersonSyn, the first large-scale dataset for multi-reference, controllable pedestrian generation, and present its automated curation pipeline which transforms public, ID-only ReID benchmarks into a richly annotated resource with the dense, multi-modal supervision required for this task. Experimental results demonstrate that OmniPerson achieves SoTA in pedestrian generation, excelling in both visual fidelity and identity consistency. Furthermore, augmenting existing datasets with our generated data consistently improves the performance of ReID models. We will open-source the full codebase, pretrained model, and the PersonSyn dataset.
Abstract (translated)
人员重识别（ReID）面临缺乏大规模高质量训练数据的问题，这主要是由于数据隐私和标注成本的挑战。虽然之前的方法探索了行人生成以进行数据增强，但它们往往无法确保身份一致性，并且控制力不足，从而限制了在数据集扩充中的有效性。为解决这一问题，我们引入了OmniPerson，这是第一个统一的身份保持型行人生成管道，适用于可见光/红外图像和视频ReID任务。 我们的贡献有三点： 1. 我们提出了OmniPerson，一个统一的生成模型，提供对所有关键行人属性的整体性和细致控制。支持RGB/IR模态图像/视频生成，并可以使用任意数量的参考图片、两种类型的人体姿态以及文本作为输入。此外还具备从RGB到IR转换和图像超分辨率能力。 2. 我们设计了多参照融合器（Multi-Refer Fuser），用于在输入任意数量的参考图片时进行稳健的身份保持，使OmniPerson能够从一组多视角参考图集中提取统一身份，并确保我们生成的行人具有高保真度的人行者生成效果。 3. 我们引入了PersonSyn，这是第一个大规模数据集，专门针对可引用的可控性行人生成。还介绍了其自动编目管道，该管道可以将仅凭ID的公共ReID基准转换为完成密集且多模态监督所需的任务丰富注释资源。 实验结果表明，OmniPerson在行人的生成方面达到了最新的技术水平，在视觉保真度和身份一致性上都表现出色。此外，通过使用我们生成的数据增强现有数据集，能够持续提升ReID模型的性能。我们将开源完整的代码库、预训练模型以及PersonSyn数据集。
URL
https://arxiv.org/abs/2512.02554
