computer-vision-8

single-image-generation

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Improved Techniques for Training Single-Image GANs

Oliver Wang, Matthew Fisher, Stefan Wermter, T. Hinz•Tue Mar 24 2020

This work conducts a number of experiments to understand the challenges of training generative models from a single image, and proposes some best practices that allowed it to generate improved results over previous work.

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PetsGAN: Rethinking Priors for Single Image Generation

Zicheng Zhang, Hailin Shi, Yinglu Liu, Congying Han, Tiande Guo, Bo Zhou•Wed Mar 02 2022

This paper digs into the single image generation problem and improves SinGAN by fully-utilization of internal and external priors, and designs a novel Prior-based end-to-end training GAN (PetsGAN), which is infused with internal prior and external prior to overcome the problems of SinGAN.

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Meta Internal Learning

Shir Gur, Lior Wolf, Raphael Bensadoun, Tomer Galanti•Tue Oct 05 2021

The results show that the models obtained are as suitable as single-image GANs for many common image applications, significantly reduce the training time per image without loss in performance, and introduce novel capabilities, such as interpolation and feedforward modeling of novel images.

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SinDDM: A Single Image Denoising Diffusion Model

T. Michaeli, V. Kulikov, S. Yadin, Matan Kleiner•Mon Nov 28 2022

This work introduces a framework for training a DDM on a single image, SinDDM, which learns the internal statistics of the training image by using a multi-scale diffusion process, and uses a fully-convolutional light-weight denoiser to drive the reverse diffusion process.

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Towards Smooth Video Composition

Bolei Zhou, Ceyuan Yang, Qihang Zhang, Yinghao Xu, Yujun Shen•Tue Dec 13 2022

This work investigates modeling the temporal relations for composing video with arbitrary length, from a few frames to even infinite, using generative adversarial networks (GANs), and develops a novel B-Spline based motion representation to ensure temporal smoothness to achieve infinite-length video generation.

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AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration

Xuefeng Xiao, Rui Wang, Xiawu Zheng, R. Ji, Minghang Zheng, Lijiang Li, Huixia Li, Jie Wu, Xin Pan, Fei Chao•Mon Sep 18 2023

This work designs a unified search space that consists of all possible time steps and various architectures and introduces a two stage evolutionary algorithm to find the optimal solution in the designed search space.

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