Disclosed are a free viewpoint video generation and interaction method based on a deep convolutional neural network. The specific steps comprise: collecting multi-viewpoint data of a target scene by using a multi-camera array synchronous photographing system that is built in a matched manner, so as to obtain a synchronous video frame sequence group at multiple viewpoints, and then carrying out pixel-level baseline calibration in batches; carrying out feature extraction on each group of input viewpoint images by using a designed and trained deep learning convolutional neural network model and by means of an encoding and decoding network structure, so as to obtain deep feature information of a scene, and respectively generating a virtual viewpoint image between each group of adjacent physical viewpoints at each moment on the basis of input images; and combining all viewpoints into a free viewpoint video frame according to the moments and the spatial positions of the viewpoints and in a matrix splicing manner. The method of the present invention does not require camera calibration and the calculation of a depth map, such that the calculation amount of virtual viewpoint generation is reduced to a large extent, and the virtual viewpoint image quality is improved by a certain extent.
FREE VIEWPOINT VIDEO GENERATION AND INTERACTION METHOD BASED ON DEEP CONVOLUTIONAL NEURAL NETWORK