Search Results for author:
Found 8 papers, 1 papers with code
GOEnFusion: Gradient Origin Encodings for 3D Forward Diffusion Models
The recently introduced Forward-Diffusion method allows to train a 3D diffusion model using only 2D images for supervision.
HoloFusion: Towards Photo-realistic 3D Generative Modeling
Diffusion-based image generators can now produce high-quality and diverse samples, but their success has yet to fully translate to 3D generation: existing diffusion methods can either generate low-resolution but 3D consistent outputs, or detailed 2D views of 3D objects but with potential structural defects and lacking view consistency or realism.
HoloDiffusion: Training a 3D Diffusion Model using 2D Images
We show that our diffusion models are scalable, train robustly, and are competitive in terms of sample quality and fidelity to existing approaches for 3D generative modeling.
3inGAN: Learning a 3D Generative Model from Images of a Self-similar Scene
We introduce 3inGAN, an unconditional 3D generative model trained from 2D images of a single self-similar 3D scene.
ReLU Fields: The Little Non-linearity That Could
Although the MLPs are able to represent complex scenes with unprecedented quality and memory footprint, this expressive power of the MLPs, however, comes at the cost of long training and inference times.
RGBD-Net: Predicting color and depth images for novel views synthesis
We propose a new cascaded architecture for novel view synthesis, called RGBD-Net, which consists of two core components: a hierarchical depth regression network and a depth-aware generator network.
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks
While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due to instability during training and sensitivity to hyperparameters.
Ranked #1 on
Image Generation
on Indian Celebs 256 x 256
AANN: Absolute Artificial Neural Network
This research paper describes a simplistic architecture named as AANN: Absolute Artificial Neural Network, which can be used to create highly interpretable representations of the input data.
