Неявные нейронные представления для 3D генерации и 3D реконструкции с нескольких точек обзораImplicit neural representations for 3D generation and multi-view 3D reconstruction

Implicit Neural Representations (INRs) recently emerged as a powerful and compact 3D representation, well fitted for the needs of differential rendering. On the other hand, basic INRs and algorithms for their optimization lack the number of qualities especially important for the tasks of multi-view reconstruction and 3D generation. These qualities include the inability of INRs to represent a large variety of objects simultaneously and excessive rendering time. Also, when combined with the generation approaches, INRs often fail to produce results which are multi-view consistent, or consistent with each other, in the case of the generation of multiple instances. In order to overcome the described drawback, a number of methods is developed and described in the current thesis: a method for training hypernetwork INR in the role of Generative Adversarial Network (GAN) generator; a method for obtaining aligned 3D models, parameterized by a single INR, given a set of text prompts; an algorithm for fast rendering and reconstruction of the implicit surface; an approach for completing the surface, which is observed only partially; a GAN-based image generation method for unsupervised shape/appearance disentanglement, where deformation maps and textures are produced by separate INR generators. Developed methods widen the scope of the application for the Implicit Neural Representations, allowing to model complex structures of the significant variety. They also allow to manipulate existing 3D objects, edit them and complete the missing parts, producing the assets which could be used in computer graphics applications.