  Virtual reality and augmented reality (XR) bring increasing demand for 3D
content. However, creating high-quality 3D content requires tedious work that a
human expert must do. In this work, we study the challenging task of lifting a
single image to a 3D object and, for the first time, demonstrate the ability to
generate a plausible 3D object with 360{\deg} views that correspond well with
the given reference image. By conditioning on the reference image, our model
can fulfill the everlasting curiosity for synthesizing novel views of objects
from images. Our technique sheds light on a promising direction of easing the
workflows for 3D artists and XR designers. We propose a novel framework, dubbed
NeuralLift-360, that utilizes a depth-aware neural radiance representation
(NeRF) and learns to craft the scene guided by denoising diffusion models. By
introducing a ranking loss, our NeuralLift-360 can be guided with rough depth
estimation in the wild. We also adopt a CLIP-guided sampling strategy for the
diffusion prior to provide coherent guidance. Extensive experiments demonstrate
that our NeuralLift-360 significantly outperforms existing state-of-the-art
baselines. Project page: https://vita-group.github.io/NeuralLift-360/
