Pixel-level Scene Understanding is one of the fundamental problems in computer vision, which aims at recognizing object classes, masks and semantics of each pixel in the given image. Since the real-world is actually video-based rather than a static state, learning to perform video semantic/panoptic segmentation is more reasonable and practical for realistic applications. To advance the semantic/panoptic segmentation task from images to videos, we present two large-scale datasets (VSPW and VIPSeg) and a competition in this workshop, aiming at performing the challenging yet practical Pixel-level Video Understanding in the Wild (PVUW).