Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

For more than a century, the methods for data representation and the exploration of the
intrinsic structures of data have developed remarkably and consist of supervised and …

We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …

Recent advances in diffusion models have set an impressive milestone in many generation
tasks, and trending works such as DALL-E2, Imagen, and Stable Diffusion have attracted …

Text-to-Image generation in the general domain has long been an open problem, which
requires both a powerful generative model and cross-modal understanding. We propose …

Energy based models (EBMs) are appealing due to their generality and simplicity in
likelihood modeling, but have been traditionally difficult to train. We present techniques to …

Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for
many natural language processing (NLP) tasks. The VAE objective consists of two terms,(i) …

Vision transformer networks have shown superiority in many computer vision tasks. In this
paper, we take a step further by proposing a novel generative vision transformer with latent …

Deep generative models are often used for human motion prediction as they are able to
model multi-modal data distributions and characterize diverse human behavior. While much …

We propose the first stochastic framework to employ uncertainty for RGB-D saliency
detection by learning from the data labeling process. Existing RGB-D saliency detection …