We propose novel motion representations for animating articulated objects consisting of
distinct parts. In a completely unsupervised manner, our method identifies object parts …

Creating high-quality articulated 3D models of animals is challenging either via manual
creation or using 3D scanning tools. Therefore, techniques to reconstruct articulated 3D …

While GANs have shown success in realistic image generation, the idea of using GANs for
other tasks unrelated to synthesis is underexplored. Do GANs learn meaningful structural …

The revolutionary breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) are
extensively being harnessed across a diverse range of domains, eg, forensic science …

The goal of self-supervised visual representation learning is to learn strong, transferable
image representations, with the majority of research focusing on object or scene level. On …

Humans can easily detect and segment moving objects simply by observing how they move,
even without knowledge of object semantics. Inspired by this, we develop a zero-shot …

Reconstructing the 3D articulated shape of an animal from a single in-the-wild image is a
challenging task. We propose LEPARD, a learning-based framework that discovers …

Automatically estimating 3D skeleton, shape, camera viewpoints, and part articulation from
sparse in-the-wild image ensembles is a severely under-constrained and challenging …

We present the first method that automatically transfers poses between stylized 3D
characters without skeletal rigging. In contrast to previous attempts to learn pose …

Unsupervised learning of keypoints and landmarks has seen significant progress with the
help of modern neural network architectures but performance is yet to match the supervised …