  Advances in 3D generation have facilitated sequential 3D model generation
(a.k.a 4D generation), yet its application for animatable objects with large
motion remains scarce. Our work proposes AnimatableDreamer, a text-to-4D
generation framework capable of generating diverse categories of non-rigid
objects on skeletons extracted from a monocular video. At its core,
AnimatableDreamer is equipped with our novel optimization design dubbed
Canonical Score Distillation (CSD), which lifts 2D diffusion for temporal
consistent 4D generation. CSD, designed from a score gradient perspective,
generates a canonical model with warp-robustness across different
articulations. Notably, it also enhances the authenticity of bones and skinning
by integrating inductive priors from a diffusion model. Furthermore, with
multi-view distillation, CSD infers invisible regions, thereby improving the
fidelity of monocular non-rigid reconstruction. Extensive experiments
demonstrate the capability of our method in generating high-flexibility
text-guided 3D models from the monocular video, while also showing improved
reconstruction performance over existing non-rigid reconstruction methods.
