Due to the difficulty in scaling up, generative adversarial networks (GA...
The success of style-based generators largely benefits from style modula...
Deep neural networks achieve superior performance for learning from
inde...
With the advance of text-to-image models (e.g., Stable Diffusion) and
co...
A good metric, which promises a reliable comparison between solutions, i...
Generative models make huge progress to the photorealistic image synthes...
Recent years witness the tremendous success of generative adversarial
ne...
Existing 3D-aware image synthesis approaches mainly focus on generating ...
Video generation requires synthesizing consistent and persistent frames ...
Discriminator plays a vital role in training generative adversarial netw...
Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on ...
Denoising Diffusion Probabilistic Models (DDPMs) have achieved impressiv...
Making generative models 3D-aware bridges the 2D image space and the 3D
...
Semi-supervised action recognition is a challenging but important task d...
The success of Generative Adversarial Networks (GANs) is largely built u...
This work aims at transferring a Generative Adversarial Network (GAN)
pr...
Generative Adversarial Networks (GANs) have significantly advanced image...
Prior research on self-supervised learning has led to considerable progr...
Generative Adversarial Networks (GANs) have recently advanced image synt...
Recent attempts for unsupervised landmark learning leverage synthesized ...
Visual tempo, which describes how fast an action goes, has shown its
pot...
Although Generative Adversarial Networks (GANs) have made significant
pr...
Visual tempo characterizes the dynamics and the temporal scale of an act...
Despite the success of Generative Adversarial Networks (GANs) in image
s...
Due to the expensive and time-consuming annotations (e.g., segmentation)...
Due to the emergence of Generative Adversarial Networks, video synthesis...