Add to ORKGWe introduce 3 IN GAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene. Such a model can be used to produce 3D “remixes” of a given scene, by mapping spatial latent codes into a 3D volumetric representation, which can subsequently be rendered from arbitrary views using physically based volume rendering. By construction, the generated scenes remain view-consistent across arbitrary camera configurations, without any flickering or spatio-temporal artifacts. During training, we employ a combination of 2D, obtained through differentiable volume tracing, and 3D Generative Adversarial Network (GAN) losses, across multiple scales, enforcing realism on both its 2D renderings and its 3D structure. We show...
We live in a world made up of different objects, people, and environments interacting with each othe...
We live in a world made up of different objects, people, and environments interacting with each othe...
Generative Adversarial Networks (GANs) have been witnessed tremendous successes in broad Computer Vi...
We introduce 3 IN GAN, an unconditional 3D generative model trained from 2D images of a single self-...
We introduce 3 IN GAN, an unconditional 3D generative model trained from 2D images of a single self-...
We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-si...
Recent advancements in generative adversarial nets (GANs) and volumetric convolutional neural networ...
Generative models, as an important family of statistical modeling, target learning the observed data...
We study the problem of synthesizing immersive 3D indoor scenes from one or a few images. Our aim is...
Recent advances in deep generative models have enabled computers to imagine and generate fictional i...
In this dissertation, we investigate the question of how 3D scenes should be represented, such that ...
Depth images can be easily acquired using depth cameras. However, these images only contain partial ...
Depth images can be easily acquired using depth cameras. However, these images only contain partial ...
In this dissertation, we investigate the question of how 3D scenes should be represented, such that ...
Expressing ideas in our minds which are inevitably visual into words had been a necessity. Lack of t...
We live in a world made up of different objects, people, and environments interacting with each othe...
We live in a world made up of different objects, people, and environments interacting with each othe...
Generative Adversarial Networks (GANs) have been witnessed tremendous successes in broad Computer Vi...
We introduce 3 IN GAN, an unconditional 3D generative model trained from 2D images of a single self-...
We introduce 3 IN GAN, an unconditional 3D generative model trained from 2D images of a single self-...
We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-si...
Recent advancements in generative adversarial nets (GANs) and volumetric convolutional neural networ...
Generative models, as an important family of statistical modeling, target learning the observed data...
We study the problem of synthesizing immersive 3D indoor scenes from one or a few images. Our aim is...
Recent advances in deep generative models have enabled computers to imagine and generate fictional i...
In this dissertation, we investigate the question of how 3D scenes should be represented, such that ...
Depth images can be easily acquired using depth cameras. However, these images only contain partial ...
Depth images can be easily acquired using depth cameras. However, these images only contain partial ...
In this dissertation, we investigate the question of how 3D scenes should be represented, such that ...
Expressing ideas in our minds which are inevitably visual into words had been a necessity. Lack of t...
We live in a world made up of different objects, people, and environments interacting with each othe...
We live in a world made up of different objects, people, and environments interacting with each othe...
Generative Adversarial Networks (GANs) have been witnessed tremendous successes in broad Computer Vi...