  Estimating 3D articulated shapes like animal bodies from monocular images is
inherently challenging due to the ambiguities of camera viewpoint, pose,
texture, lighting, etc. We propose ARTIC3D, a self-supervised framework to
reconstruct per-instance 3D shapes from a sparse image collection in-the-wild.
Specifically, ARTIC3D is built upon a skeleton-based surface representation and
is further guided by 2D diffusion priors from Stable Diffusion. First, we
enhance the input images with occlusions/truncation via 2D diffusion to obtain
cleaner mask estimates and semantic features. Second, we perform
diffusion-guided 3D optimization to estimate shape and texture that are of
high-fidelity and faithful to input images. We also propose a novel technique
to calculate more stable image-level gradients via diffusion models compared to
existing alternatives. Finally, we produce realistic animations by fine-tuning
the rendered shape and texture under rigid part transformations. Extensive
evaluations on multiple existing datasets as well as newly introduced noisy web
image collections with occlusions and truncation demonstrate that ARTIC3D
outputs are more robust to noisy images, higher quality in terms of shape and
texture details, and more realistic when animated. Project page:
https://chhankyao.github.io/artic3d/
