In this paper, we focus on the task of generalizable neural human rendering which trains
conditional Neural Radiance Fields (NeRF) from multi-view videos of different characters. To …

Abstract 3D understanding and rendering of moving humans from monocular videos is a
challenging task. Although recent progress has enabled this task to some extent, it is still …

Existing self-supervised 3D human pose estimation schemes have largely relied on weak
supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior …

This work targets at using a general deep learning framework to synthesize free-viewpoint
images of arbitrary human performers, only requiring a sparse number of camera views as …

We introduce HOSNeRF, a novel 360deg free-viewpoint rendering method that reconstructs
neural radiance fields for dynamic human-object-scene from a single monocular in-the-wild …

Recent advances in 3D-aware GAN models have enabled the generation of realistic and
controllable human body images. However, existing methods focus on the control of major …

In this work, we focus on synthesizing high-fidelity novel view images for arbitrary human
performers, given a set of sparse multi-view images. It is a challenging task due to the large …

In this era the success of large language models and text-to-image models can be attributed
to the driving force of large-scale datasets. However in the realm of 3D vision while …

We propose a new method for learning a generalized animatable neural human
representation from a sparse set of multi-view imagery of multiple persons. The learned …

The past two years have witnessed a significant increase in interest concerning NeRF-
based human body rendering. While this surge has propelled considerable advancements …