3D GANs and Latent Space: A comprehensive survey
Generative Adversarial Networks (GANs) have emerged as a significant player in generative modeling by mapping lower-dimensional random noise to higher-dimensional spaces. These networks have been used to generate high-resolution images and 3D objects. The efficient modeling of 3D objects and human faces is crucial in the development process of 3D graphical environments such as games or simulations. 3D GANs are a new type of generative model used for 3D reconstruction, point cloud reconstruction, and 3D semantic scene completion. The choice of distribution for noise is critical as it represents the latent space. Understanding a GAN's latent space is essential for fine-tuning the generated samples, as demonstrated by the morphing of semantically meaningful parts of images. In this work, we explore the latent space and 3D GANs, examine several GAN variants and training methods to gain insights into improving 3D GAN training, and suggest potential future directions for further research.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Reconstruction3D Semantic Scene CompletionPoint cloud reconstructionSurveySimilar Papers 제목 키워드 기반
A Comprehensive Survey on Data-Efficient GANs in Image Generation
Generative Adversarial Networks (GANs) have achieved remarkable achievements in image synthesis. These successes of GANs rely on large scale datasets, requiring too much cost. With limited training data, how to stable th…
Image GenerationSurveyImage Generation and Recognition (Emotions)
Generative Adversarial Networks (GANs) were proposed in 2014 by Goodfellow et al., and have since been extended into multiple computer vision applications. This report provides a thorough survey of recent GAN research, o…
Image GenerationRevisiting Latent Space of GAN Inversion for Real Image Editing
The exploration of the latent space in StyleGANs and GAN inversion exemplify impressive real-world image editing, yet the trade-off between reconstruction quality and editing quality remains an open problem. In this stud…
Unsupervised Image Generation with Infinite Generative Adversarial Networks
Image generation has been heavily investigated in computer vision, where one core research challenge is to generate images from arbitrarily complex distributions with little supervision. Generative Adversarial Networks (…
Image GenerationTransformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey
Generative Adversarial Networks (GANs) have been very successful for synthesizing the images in a given dataset. The artificially generated images by GANs are very realistic. The GANs have shown potential usability in se…
Image GenerationImage-to-Image TranslationTranslation