  Existing neural rendering methods for creating human avatars typically either
require dense input signals such as video or multi-view images, or leverage a
learned prior from large-scale specific 3D human datasets such that
reconstruction can be performed with sparse-view inputs. Most of these methods
fail to achieve realistic reconstruction when only a single image is available.
To enable the data-efficient creation of realistic animatable 3D humans, we
propose ELICIT, a novel method for learning human-specific neural radiance
fields from a single image. Inspired by the fact that humans can effortlessly
estimate the body geometry and imagine full-body clothing from a single image,
we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior.
Specifically, ELICIT utilizes the 3D body shape geometry prior from a skinned
vertex-based template model (i.e., SMPL) and implements the visual clothing
semantic prior with the CLIP-based pretrained models. Both priors are used to
jointly guide the optimization for creating plausible content in the invisible
areas. Taking advantage of the CLIP models, ELICIT can use text descriptions to
generate text-conditioned unseen regions. In order to further improve visual
details, we propose a segmentation-based sampling strategy that locally refines
different parts of the avatar. Comprehensive evaluations on multiple popular
benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT
has outperformed strong baseline methods of avatar creation when only a single
image is available. The code is public for research purposes at
https://huangyangyi.github.io/ELICIT/.
