Articulated Kinematics Distillation from Video Diffusion Models
Authors: name: Xuan Li, name: Qianli Ma, name: Tsung-Yi Lin, name: Yongxin Chen, name: Chenfanfu Jiang, name: Ming-Yu Liu, name: Donglai Xiang

We present Articulated Kinematics Distillation (AKD), a framework for
generating high-fidelity character animations by merging the strengths of
skeleton-based animation and modern generative models. AKD uses a
skeleton-based representation for rigged 3D assets, drastically reducing the
Degrees of Freedom (DoFs) by focusing on joint-level control, which allows for
efficient, consistent motion synthesis. Through Score Distillation Sampling
(SDS) with pre-trained video diffusion models, AKD distills complex,
articulated motions while maintaining structural integrity, overcoming
challenges faced by 4D neural deformation fields in preserving shape
consistency. This approach is naturally compatible with physics-based
simulation, ensuring physically plausible interactions. Experiments show that
AKD achieves superior 3D consistency and motion quality compared with existing
works on text-to-4D generation. Project page:
https://research.nvidia.com/labs/dir/akd/