i3D Robotics (United Kingdom)

Most motion deblurring algorithms rely on spatial-domain convolution models, which struggle with the complex, nonlinear blur arising from camera shake and object motion. In contrast, we propose a novel single-image deblurring approach that treats motion blur as a temporal averaging phenomenon. Our core innovation lies in leveraging a pre-trained video diffusion transformer model to capture diverse motion dynamics within a latent space. It sidesteps explicit kernel estimation and effectively accommodates diverse motion patterns. We implement the algorithm within a diffusion-based inverse problem framework. Empirical results on synthetic and real-world datasets demonstrate that our method outperforms existing techniques in deblurring complex motion blur scenarios. This work paves the way for utilizing powerful video diffusion models to address single-image deblurring challenges.

Examines the developing uses of off‐line programming (OLP) in the automotive industry, particularly in the areas of robot welding and robot painting of vehicle bodies. States that this is due to the development of graphical simulation tools and outlines their role in “right first time” policies. Looks at applications of OLP at Volvo, Capponi Alesina (Fiat’s largest, painting subcontractor) and Applied Manufacturing Technologies, a US manufacturing systems design house. Describes the use of OLP in painting machines and truck painting, and in spot welding truck bodies.