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

NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360{\deg} Views