137 datasets found

Diffusion MRI (dMRI) is an essential neuroimaging technique with high acquisition costs. Deep learning approaches have been used to enhance dMRI and predict diffusion biomarkers...

Diffusion models have recently emerged as the state-of-the-art of generative modeling, demonstrating remarkable results in image synthesis and across other modalities.

SDXL: Improving latent diffusion models for high-resolution image synthesis.

The dataset used for training and testing the Conditionally-Independent Pixel Synthesis (CIPS) generator.

The dataset contains five tabletop real scenes imaged by 352 cameras in a dome.

The dataset contains 80 real-world scenes with image sequences and structured light 3D scans as reference data.

Large-scale text-to-image generation models have achieved remarkable progress in synthesizing high-quality, feature-rich images with high resolution guided by texts.

The dataset used in the paper is LAION-2B, which is a large-scale image-text dataset. The authors fine-tune a pre-trained diffusion model with a subset of LAION-2B with 10k...

Face swapping aims at injecting a source image’s identity (i.e., facial features) into a target image, while strictly preserving the target’s attributes, which are irrelevant to...

Text-conditioned image generation has made significant progress in recent years with generative adversarial networks and more recently, diffusion models. The proposed guidance...

LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation

Zero-1-to-3: Zero-shot one image to 3D object.

The dataset used in the paper is a set of images from the AFHQ dataset, containing 1.5K images of different animal faces.

A dataset for refining malformed hands with decoupled structure and style guidance.

A dataset for refining malformed hands with decoupled structure and style guidance.

A dataset for refining malformed hands with decoupled structure and style guidance.

A dataset for refining malformed hands with decoupled structure and style guidance.

The authors used MNIST, SVHN, and CIFAR10 datasets for their experiments.

The dataset used in the paper is a high-resolution image synthesis dataset, which consists of images generated using a latent diffusion model.

The dataset used in the paper is a large set of real images extracted from the RAISE dataset.