DIMO: Diverse 3D Motion Generation for Arbitrary Objects
Authors: name: Linzhan Mou, name: Jiahui Lei, name: Chen Wang, name: Lingjie Liu, name: Kostas Daniilidis

We present DIMO, a generative approach capable of generating diverse 3D
motions for arbitrary objects from a single image. The core idea of our work is
to leverage the rich priors in well-trained video models to extract the common
motion patterns and then embed them into a shared low-dimensional latent space.
Specifically, we first generate multiple videos of the same object with diverse
motions. We then embed each motion into a latent vector and train a shared
motion decoder to learn the distribution of motions represented by a structured
and compact motion representation, i.e., neural key point trajectories. The
canonical 3D Gaussians are then driven by these key points and fused to model
the geometry and appearance. During inference time with learned latent space,
we can instantly sample diverse 3D motions in a single-forward pass and support
several interesting applications including 3D motion interpolation and
language-guided motion generation. Our project page is available at
https://linzhanm.github.io/dimo.