Wonderland: Navigating 3D Scenes from a Single Image
Authors: name: Hanwen Liang, name: Junli Cao, name: Vidit Goel, name: Guocheng Qian, name: Sergei Korolev, name: Demetri Terzopoulos, name: Konstantinos N. Plataniotis, name: Sergey Tulyakov, name: Jian Ren

This paper addresses a challenging question: How can we efficiently create
high-quality, wide-scope 3D scenes from a single arbitrary image? Existing
methods face several constraints, such as requiring multi-view data,
time-consuming per-scene optimization, low visual quality in backgrounds, and
distorted reconstructions in unseen areas. We propose a novel pipeline to
overcome these limitations. Specifically, we introduce a large-scale
reconstruction model that uses latents from a video diffusion model to predict
3D Gaussian Splattings for the scenes in a feed-forward manner. The video
diffusion model is designed to create videos precisely following specified
camera trajectories, allowing it to generate compressed video latents that
contain multi-view information while maintaining 3D consistency. We train the
3D reconstruction model to operate on the video latent space with a progressive
training strategy, enabling the efficient generation of high-quality,
wide-scope, and generic 3D scenes. Extensive evaluations across various
datasets demonstrate that our model significantly outperforms existing methods
for single-view 3D scene generation, particularly with out-of-domain images.
For the first time, we demonstrate that a 3D reconstruction model can be
effectively built upon the latent space of a diffusion model to realize
efficient 3D scene generation.