Learning Shape Representations for Clothing Variations in Person Re-Identification

TL;DR

This paper introduces CASE-Net, a novel model that learns clothing-invariant body shape features for person re-identification, addressing clothing change challenges with synthetic datasets and demonstrating superior robustness over existing methods.

Contribution

The paper presents CASE-Net, a new adversarial learning model that extracts clothing-agnostic shape features and introduces synthetic datasets for evaluating clothing change scenarios in re-ID.

Findings

CASE-Net outperforms state-of-the-art methods on multiple datasets.

Synthetic datasets effectively simulate clothing variations for training and evaluation.

Shape-based features improve re-ID robustness under clothing changes.

Abstract

Person re-identification (re-ID) aims to recognize instances of the same person contained in multiple images taken across different cameras. Existing methods for re-ID tend to rely heavily on the assumption that both query and gallery images of the same person have the same clothing. Unfortunately, this assumption may not hold for datasets captured over long periods of time (e.g., weeks, months or years). To tackle the re-ID problem in the context of clothing changes, we propose a novel representation learning model which is able to generate a body shape feature representation without being affected by clothing color or patterns. We call our model the Color Agnostic Shape Extraction Network (CASE-Net). CASE-Net learns a representation of identity that depends only on body shape via adversarial learning and feature disentanglement. Due to the lack of large-scale re-ID datasets which contain clothing changes for the same person, we propose two synthetic datasets for evaluation. We create a rendered dataset SMPL-reID with different clothes patterns and a synthesized dataset Div-Market with different clothing color to simulate two types of clothing changes. The quantitative and qualitative results across 5 datasets (SMPL-reID, Div-Market, two benchmark re-ID datasets, a cross-modality re-ID dataset) confirm the robustness and superiority of our approach against several state-of-the-art approaches