In this section, we provide detailed derivation for the kinematics proposed in the main paper. The numbers in () indicate the dimension of output features. S is a shape matrix that we set to the identity matrix I in LEP ARD since we use one-to-one mapping for the local deformation estimation. Finally, we obtain a pseudo ground-truth object silhouette G by thresholding the minimum feature distance to the center of the clusters. In Figure 1, we provide the architecture of the encoder-decoder model proposed in the main paper.

Therefore,techniquestoreconstruct articulated 3D objects from 2D images are crucial and highly useful. In this work, we propose a practical problem setting to estimate 3D pose and shape of animals given only a few (10-30) in-the-wild images of a particular animal species (say,horse). Contrary toexisting worksthatrelyonpre-defined template shapes, we do not assume any form of 2D or 3D ground-truth annotations, nor do we leverage any multi-view or temporal information. Moreover, each input image ensemble can contain animal instances with varying poses, backgrounds, illuminations, and textures. Our key insight is that 3D parts have much simpler shape compared totheoverall animal and that theyarerobustw.r.t.

Moreover, each input image ensemble can contain animal instances with varying poses, backgrounds, illuminations, and textures. Our key insight is that 3D parts have much simpler shape compared to the overall animal and that they are robust w.r.t.