DIMO: Diverse 3D Motion Generation for Arbitrary Objects
This addresses the challenge of creating varied 3D animations for objects in computer vision and graphics, though it appears incremental as it builds on existing video models and motion representation techniques.
The paper tackles the problem of generating diverse 3D motions for arbitrary objects from a single image by leveraging video model priors to extract motion patterns into a latent space, resulting in a method that can sample diverse 3D motions in a single-forward pass and support applications like interpolation and language-guided generation.
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.