Object Agnostic 3D Lifting in Space and Time

We present a spatio-temporal perspective on category-agnostic 3D lifting of 2D keypoints over a temporal se-quence. Our approach differs from existing state-of-the-art methods that are either: (i) object-agnostic, but can only operate on individual frames, or (ii) can model space-time dependencies, but are only designed to work with a single object category. Our approach is grounded in two core prin-ciples. First, general information about similar objects can be leveraged to achieve better performance when there is little object-specific training data. Second, a temporally-proximate context window is advantageous for achieving consistency throughout a sequence. These two principles allow us to outperform current state-of-the-art methods on per-frame and per-sequence metrics for a variety of animal categories. Lastly, we release a new synthetic dataset con-taining 3D skeletons and motion sequences for a variety of animal categories.