This review thoroughly examines the role of semantically-aware Neural Radiance Fields
(NeRFs) in visual scene understanding, covering an analysis of over 250 scholarly papers. It …

Image and video synthesis has become a blooming topic in computer vision and machine
learning communities along with the developments of deep generative models, due to its …

We present HumanNeRF-SE a simple yet effective method that synthesizes diverse novel
pose images with simple input. Previous HumanNeRF works require a large number of …

Recently, implicit neural representations (INRs) have attracted increasing attention for multi-
dimensional data recovery. However, INRs simply map coordinates via a multi-layer …

Rendering the visual appearance of moving humans from occluded monocular videos is a
challenging task. Most existing research renders 3D humans under ideal conditions …

Estimating the 3D structure of the human body from nat-ural scenes is a fundamental aspect
of visual perception. 3D human pose estimation is a vital step in advancing fields like AIGC …

Abstract We introduce MIGS (Multi-Identity Gaussian Splatting), a novel method that learns a
single neural representation for multiple identities, using only monocular videos. Recent 3D …

Recent techniques on implicit geometry representation learning and neural rendering have
shown promising results for 3D clothed human reconstruction from sparse video inputs …

We present a novel framework to reconstruct complete 3D human shapes from a given
target image by leveraging monocular unconstrained images. The objective of this work is to …

While numerous studies have explored NeRF-based novel view synthesis for dynamic
humans, they often require training that exceeds several hours, limiting their practicality …