Neural Radiance Field (NeRF) and its variants have exhibited great success on
representing 3D scenes and synthesizing photo-realistic novel views. However, they are …

Diffusion models have revolutionized text-driven video editing. However, applying these
methods to real-world editing encounters two significant challenges:(1) the rapid increase in …

Neural fields have emerged as a powerful and broadly applicable method for representing
signals. However in contrast to classical discrete digital signal processing the portfolio of …

Cameras with a finite aperture diameter exhibit defocus for scene elements that are not at
the focus distance, and have only a limited depth of field within which objects appear …

Neural fields are evolving towards a general-purpose continuous representation for visual
computing. Yet, despite their numerous appealing properties, they are hardly amenable to …

Abstract Dynamic Neural Radiance Field (NeRF) from monocular videos has recently been
explored for space-time novel view synthesis and achieved excellent results. However …

Abstract Recent advances in Neural Radiance Field (NeRF) show promising results in
rendering realistic novel view images. However, NeRF and its variants assume that input …

Single image-to-3D generation is pivotal for crafting controllable 3D assets. Given its
underconstrained nature, we leverage geometric priors from a 3D novel view generation …

Object tracking stands as a cornerstone challenge within computer vision, with blurriness
analysis representing a burgeoning field of interest. Among the various forms of blur …

Recently, there have been significant developments in the realm of novel view synthesis
relying on radiance fields. By incorporating the Splatting technique, a new approach named …