Image generation method based on shape space theory

The invention relates to an image generation method based on a shape space theory, and the method comprises the following steps: inputting an image data set and random noise, constructing an image generation model and a pre-shape space, the image generation model comprising a generator and a discriminator; randomly selecting a group of images from the image data set, inputting the images into a discriminator to obtain training features, projecting the training features to obtain data features, constructing a geodesic curved surface on a pre-shape space, and obtaining sample features according to randomly generated sampling weights; generating random noise through Gaussian distribution sampling, calculating anchor point features through the random noise and sample features, constructing an anchor point image and an interpolation image, and extracting the anchor point features and interpolation image features; constructing loss through sample features, anchor point features and interpolation image features, and