Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Denoising diffusion models embody a type of generative artificial intelligence that can be
applied in computer vision, natural language processing and bioinformatics. In this Review …

Abstract Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses
RGB images and LiDAR data by depth completion has gained great attention. However …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Diffusion models have revolutionized text-to-image generation with its exceptional quality
and creativity. However, its multi-step sampling process is known to be slow, often requiring …

Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …

Most existing point cloud upsampling methods have roughly three steps: feature extraction,
feature expansion and 3D coordinate prediction. However, they usually suffer from two …

Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose,
a versatile framework to model plausible 3D human poses for various applications. At the …

The paper presents a novel approach for vector-floorplan generation via a diffusion model,
which denoises 2D coordinates of room/door corners with two inference objectives: 1) a …

Chamfer distance (CD) is a standard metric to measure the shape dissimilarity between
point clouds in point cloud completion, as well as a loss function for (deep) learning …