We investigate how to generate multimodal image outputs, such as RGB, de...
Denoising Diffusion models have shown remarkable capabilities in generat...
We present multimodal conditioning modules (MCM) for enabling conditiona...
Large-scale text-to-image generative models have shown their remarkable
...
The vision community has explored numerous pose guided human editing met...
The task of human reposing involves generating a realistic image of a pe...
We introduce a new method for diverse foreground generation with explici...
Existing GAN inversion and editing methods work well for aligned objects...
3D-aware generative models have shown that the introduction of 3D inform...
While GANs can produce photo-realistic images in ideal conditions for ce...
Synthesizing dynamic appearances of humans in motion plays a central rol...
We propose a new approach for high resolution semantic image synthesis. ...
We introduce an inversion based method, denoted as IMAge-Guided model
IN...
We consider the novel task of learning disentangled representations of o...
Existing models often leverage co-occurrences between objects and their
...
We present MixNMatch, a conditional generative model that learns to
dise...
We present MixNMatch, a conditional generative model that learns to
dise...
We propose a novel unsupervised generative model, Elastic-InfoGAN, that
...
We propose FineGAN, a novel unsupervised GAN framework, which disentangl...
We propose 'Hide-and-Seek' a general purpose data augmentation technique...
We propose the idea of transferring common-sense knowledge from source
c...
Content popularity prediction has been extensively studied due to its
im...
We propose an end-to-end deep convolutional network to simultaneously
lo...
The status quo approach to training object detectors requires expensive
...