  Person re-identification (Re-ID) often faces challenges due to variations in
human poses and camera viewpoints, which significantly affect the appearance of
individuals across images. Existing datasets frequently lack diversity and
scalability in these aspects, hindering the generalization of Re-ID models to
new camera systems. Previous methods have attempted to address these issues
through data augmentation; however, they rely on human poses already present in
the training dataset, failing to effectively reduce the human pose bias in the
dataset. We propose Diff-ID, a novel data augmentation approach that
incorporates sparse and underrepresented human pose and camera viewpoint
examples into the training data, addressing the limited diversity in the
original training data distribution. Our objective is to augment a training
dataset that enables existing Re-ID models to learn features unbiased by human
pose and camera viewpoint variations. To achieve this, we leverage the
knowledge of pre-trained large-scale diffusion models. Using the SMPL model, we
simultaneously capture both the desired human poses and camera viewpoints,
enabling realistic human rendering. The depth information provided by the SMPL
model indirectly conveys the camera viewpoints. By conditioning the diffusion
model on both the human pose and camera viewpoint concurrently through the SMPL
model, we generate realistic images with diverse human poses and camera
viewpoints. Qualitative results demonstrate the effectiveness of our method in
addressing human pose bias and enhancing the generalizability of Re-ID models
compared to other data augmentation-based Re-ID approaches. The performance
gains achieved by training Re-ID models on our offline augmented dataset
highlight the potential of our proposed framework in improving the scalability
and generalizability of person Re-ID models.
