Neural implicit function based on signed distance field (SDF) has achieved impressiveprogress inreconstructing 3Dmodels withhighfidelity. However,such approaches canonlyrepresent closed surfaces.

To decouple the learning of underlying scene geometry from dynamic motion, we represent the scene as a time-invariantsigneddistance function (SDF)whichservesasareference frame, along with a time-conditioned deformation field.

Simulating large scenes with many rigid objects is crucial for a variety of applications, such as robotics, engineering, film and video games. Rigid interactions are notoriously hard to model: small changes to the initial state or the simulation parameters can lead to large changes in the final state.

Overall, our method can generate high-fidelity, diverse, and multi-view consistent meshes from single-view wild images within 30 seconds, as shown in Figure 1. We conduct extensive experiments on various wild 2D images with different styles.