WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality (AR) Technology provider, announced that its R&D team is working on algorithms for 3D image generation based on Generative Adversarial Networks. Generative Adversarial Network (GAN) is an effective model for generating data and creating intelligence. The basic GAN model consists structurally of a Generator and a Discriminator. The initial purpose of GAN is to perform unsupervised learning based on large amounts of unlabeled data, which has the ability to generate data in various forms (image, speech, language, etc.).
The algorithm generates realistic 3D images by means of adversarial training. The 3D generation of the generative adversarial network is achieved by adversarial training of a generator and a discriminator. The generator is responsible for generating realistic 3D models, while the discriminator is responsible for determining whether the 3D models generated by the generator are realistic or not. During the training process, the generator keeps generating 3D models and the discriminator keeps judging their realism until the 3D models generated by the generator cannot be distinguished by the discriminator, at which time the training of the generator is completed. The generator can generate different 3D models, thus realizing the diversity of 3D models.
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The steps of 3D image generation for GAN mainly include:
Data preparation: prepare the 3D model dataset for training, which can be a real 3D model or a virtual 3D model.
Structure design: design the network structure of the generator and discriminator. The generator is responsible for generating realistic 3D models, and the discriminator is responsible for judging whether the 3D models generated by the generator are realistic or not.
Training the model: the generator and the discriminator are trained using the prepared dataset. In training, the generator keeps generating 3D models and the discriminator keeps judging their authenticity until the 3D models generated by the generator cannot be distinguished by the discriminator, at which point the training of the generator is completed.
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Optimize the model: Use optimization algorithms to optimize the generated 3D model to make it more lifelike and realistic.
With the continuous “evolution” of the “generative adversarial network” technology, it has been expanded from the traditional computer vision to other directions, in the confrontation samples, data augmentation, migration learning and creation of intelligence, etc. have shown great potential, and has become a new trend of deep learning and artificial intelligence technology.
WiMi‘s 3D image generation algorithm based on generative adversarial networks has a wide range of applications and can provide important technical support for game development, virtual reality, architectural design and other fields. In game development, adversarial networks can be used to generate realistic 3D character models, scene models, etc., to enhance the realism and playability of the game. In virtual reality, the GAN can be used to generate realistic 3D scene models to enhance the immersion of virtual reality.In architectural design, it can be used to generate realistic 3D building models to help designers carry out architectural design and planning.
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