  Compared to visible-to-visible (V2V) person re-identification (ReID), the
visible-to-infrared (V2I) person ReID task is more challenging due to the lack
of sufficient training samples and the large cross-modality discrepancy.
  To this end, we propose Flow2Flow, a unified framework that could jointly
achieve training sample expansion and cross-modality image generation for V2I
person ReID.
  Specifically, Flow2Flow learns bijective transformations from both the
visible image domain and the infrared domain to a shared isotropic Gaussian
domain with an invertible visible flow-based generator and an infrared one,
respectively.
  With Flow2Flow, we are able to generate pseudo training samples by the
transformation from latent Gaussian noises to visible or infrared images, and
generate cross-modality images by transformations from existing-modality images
to latent Gaussian noises to missing-modality images.
  For the purpose of identity alignment and modality alignment of generated
images, we develop adversarial training strategies to train Flow2Flow.
  Specifically, we design an image encoder and a modality discriminator for
each modality.
  The image encoder encourages the generated images to be similar to real
images of the same identity via identity adversarial training, and the modality
discriminator makes the generated images modal-indistinguishable from real
images via modality adversarial training.
  Experimental results on SYSU-MM01 and RegDB demonstrate that both training
sample expansion and cross-modality image generation can significantly improve
V2I ReID accuracy.
