Generating animated 3D objects is at the heart of many applications, yet most advanced works are
typically difficult to apply in practice because of their limited setup, their long runtime, or their
limited quality.
We introduce ActionMesh, a generative model that predicts production-ready 3D meshes "in action" in a
feed-forward manner.
Drawing inspiration from early video models, our key insight is to modify existing 3D diffusion models
to include a temporal axis, resulting in a framework we dubbed "temporal 3D diffusion".
Specifically, we first adapt the 3D diffusion stage to generate a sequence of synchronized latents
representing time-varying and independent 3D shapes.
Second, we design a temporal 3D autoencoder that translates a sequence of independent shapes into the
corresponding deformations of a pre-defined reference shape, allowing us to build an animation.
Combining these two components, ActionMesh generates animated 3D meshes from different inputs like a
monocular video, a text description, or even a 3D mesh with a text prompt describing its animation.
Besides, compared to previous approaches, our method is fast and produces results that are rig-free and
topology consistent, hence enabling rapid iteration and seamless applications like texturing and
retargeting.
We evaluate our model on standard video-to-4D benchmarks (Consistent4D, Objaverse) and report
state-of-the-art performances on both geometric accuracy and temporal consistency, demonstrating that
our model can deliver animated 3D meshes with unprecedented speed and quality.
