View DiffGait: View Pyramid Diffusion for Gait Recognition

View transformation is crucial for gait recognition. Most existing methods use a view transformation models (VTM) or generative models (VAE or GAN) to achieve transformation. These approaches commonly adopt a paradigm of transforming a gait feature from one view to another. However, most existing methods attempt to use a single or multiple, large-view transformation model to directly transform a source view image to the target view image. Such transformations usually suffer from precision problems under large viewpoint variations due to the lack of fine view prediction. To overcome this challenge, we introduce a novel framework, ViewDiffGait, employing a view pyramid structure and diffusion models. ViewDiffGait is formulated as an iterative refinement generation task in a biologically interpretable way, capable of generating more accurate lateral view images from coarse to fine. Unlike the typical diffusion model that directly adds and removes Gaussian noise in the original image, the ViewDiffGait diffusion process involves a view pyramid structure to capture fine view transformations. The diffusion process adds view noise from the pyramid top to the bottom, while the denoising process removes view noise from the pyramid bottom to the top. We conducted extensive experiments on the CASIA-B and OUMVLP datasets, demonstrating that ViewDiffGait can generate more realistic images, remove variations effectively, and lead to high performance in real applications.