  Reconstructing dynamic articulated objects from a singular monocular video is
challenging, requiring joint estimation of shape, motion, and camera parameters
from limited views. Current methods typically demand extensive computational
resources and training time, and require additional human annotations such as
predefined parametric models, camera poses, and key points, limiting their
generalizability. We propose Synergistic Shape and Skeleton Optimization (S3O),
a novel two-phase method that forgoes these prerequisites and efficiently
learns parametric models including visible shapes and underlying skeletons.
Conventional strategies typically learn all parameters simultaneously, leading
to interdependencies where a single incorrect prediction can result in
significant errors. In contrast, S3O adopts a phased approach: it first focuses
on learning coarse parametric models, then progresses to motion learning and
detail addition. This method substantially lowers computational complexity and
enhances robustness in reconstruction from limited viewpoints, all without
requiring additional annotations. To address the current inadequacies in 3D
reconstruction from monocular video benchmarks, we collected the PlanetZoo
dataset. Our experimental evaluations on standard benchmarks and the PlanetZoo
dataset affirm that S3O provides more accurate 3D reconstruction, and plausible
skeletons, and reduces the training time by approximately 60% compared to the
state-of-the-art, thus advancing the state of the art in dynamic object
reconstruction.
