With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

For the last few decades, several major subfields of artificial intelligence including computer
vision, graphics, and robotics have progressed largely independently from each other …

We introduce DMTet, a deep 3D conditional generative model that can synthesize high-
resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits …

Collecting and annotating images with pixel-wise labels is time-consuming and laborious. In
contrast, synthetic data can be freely available using a generative model (eg, DALL-E …

Current state-of-the-art methods for image segmentation form a dense image representation
where the color, shape and texture information are all processed together inside a deep …

Diffusion Probabilistic Methods are employed for state-of-the-art image generation. In this
work, we present a method for extending such models for performing image segmentation …

High-definition (HD) map provides abundant and precise static environmental information of
the driving scene, serving as a fundamental and indispensable component for planning in …

Image segmentation is one of the most basic tasks in computer vision and remains an initial
step of many applications. In this paper, we focus on interactive image segmentation (IIS) …

Large-scale training data with high-quality annotations is critical for training semantic and
instance segmentation models. Unfortunately, pixel-wise annotation is labor-intensive and …

High-definition (HD) map provides abundant and precise environmental information of the
driving scene, serving as a fundamental and indispensable component for planning in …