We address the problem of synthesizing novel views from a monocular video depicting a
complex dynamic scene. State-of-the-art methods based on temporally varying Neural …

We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes. Prior work
tends to reconstruct single video clips of short durations (up to 10 seconds). Two reasons …

Visually exploring in a real-world 4D spatiotemporal space freely in VR has been a long-
term quest. The task is especially appealing when only a few or even single RGB cameras …

We study the recent progress on dynamic view synthesis (DVS) from monocular video.
Though existing approaches have demonstrated impressive results, we show a discrepancy …

In recent years, neural implicit surface reconstruction has emerged as a popular paradigm
for multi-view 3D reconstruction. Unlike traditional multi-view stereo approaches, the neural …

Neural radiance fields (NeRF) excel at synthesizing new views given multi-view, calibrated
images of a static scene. When scenes include distractors, which are not persistent during …

Radiance Fields (RF) are popular to represent casually-captured scenes for new view
synthesis and several applications beyond it. Mixed reality on personal spaces needs …

This paper presents a critical analysis of image-based 3D reconstruction using neural
radiance fields (NeRFs), with a focus on quantitative comparisons with respect to traditional …

Text-driven localized editing of 3D objects is particularly difficult as locally mixing the original
3D object with the intended new object and style effects without distorting the object's form is …

Neural rendering combines ideas from classical computer graphics and machine learning to
synthesize images from real-world observations. NeRF, short for Neural Radiance Fields, is …