WePerson: Learning a Generalized Re-identification Model from All-weather Virtual Data

The aim of person re-identification (Re-ID) is retrieving a person of interest across multiple non-overlapping cameras. Re-ID has gained significantly increased advancement in recent years. However, real data annotation is costly and model generalization ability is hindered by the lack of large-scale and diverse data. To address this problem, we propose a Weather Person pipeline that can generate a synthesized Re-ID dataset with different weather, scenes, and natural lighting conditions automatically. The pipeline is built on the top of a game engine which contains a digital city, weather and lighting simulation system, and various character models with manifold dressing. To train a generalizable Re-ID model from the large-scale virtual WePerson dataset, we design an adaptive sample selection strategy to close the domain gap and avoid redundancy. We also design an informative sampling method for a mini-batch sampler to accelerate the learning process. In addition, an efficient training method is introduced by adopting instance normalization to capture identity invariant components from various appearances. We evaluate our pipeline using direct transfer on 3 widely-used real-world benchmarks, achieving competitive performance without any real-world image training. This dataset starts the attempt to evaluate diverse environmental factors in a controllable virtual engine, which provides important guidance for future generalizable Re-ID model design. Notably, we improve the current state-of-the-art accuracy from 38.5% to 46.4% on the challenging MSMT17 dataset. Dataset and code are available at https://github.com/lihe404/WePerson https://github.com/lihe404/WePerson.