Luc Van Gool

TL;DR: This paper addresses the issue of learning classspecific, deformable, 3D part-based structure for object part localization in 3D models/scenes with promise for application in more complex 3D processing tasks such as part retrieval, pose estimation, scene understanding and recognition.

TL;DR: This paper addresses the problem of effective visualization of and interaction with multiple and multi-dimensional data supporting communication between project stakeholders in an information cave by developing a novel human-computer interaction system specifically targeted towards room setups with physically spread sets of screens.

TL;DR: Sinusoidal Neural Radiance Fields (SiNeRF) is proposed that leverage sinusoidal activations for radiance mapping and a novel Mixed Region Sampling (MRS) for selecting ray batch efficiently and achieves comprehensive improvements in image synthesis quality and pose estimation accuracy.

TL;DR: This work proposes a joint framework of diversity and multi-mapping image-to-image translations, using a single generator to conditionally produce countless and unique fake images that hold the underlying characteristics of the source image.

TL;DR: The proposed 3DCF method uses the exact same convolutional network architecture to address both image denoising and single image super-resolution, and achieves substantial improvements over the state of the art methods that it fuses on standard benchmarks for both tasks.