We propose a new algorithm called Generic Cuts for computing optimal solutions to 2 label MRF-MAP problems with higher order clique potentials satisfying submodularity. The algorithm runs in time O(2kn3) in the worst case (k is clique order and n is the number of pixels). A special gadget is introduced to model flows in a high order clique and a technique for building a flow graph is specified. Based on the primal dual structure of the optimization problem the notions of capacity of an edge and cut are generalized to define a flow problem. We show that in this flow graph max flow is equal to min cut which also is the optimal solution to the problem when potentials are submodular. This is in contrast to all prevalent techniques of optimizing Boolean energy functions involving higher order potentials including those based on reductions to quadratic potential functions which provide only approximate solutions even for submodular functions. We show experimentally that our implementation of the Generic Cuts algorithm is more than an order of magnitude faster than all algorithms including reduction based whose outputs on submodular potentials are near optimal.

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Generic cuts: an efficient algorithm for optimal inference in higher order MRF-MAP

We present a method to build animatable dog avatars from monocular videos. This is challenging as animals display a range of (unpredictable) non-rigid movements and have a variety of appearance details (e.g., fur, spots, tails). We develop an approach that links the video frames via a 4D solution that jointly solves for animal's pose variation, and its appearance (in a canonical pose). To this end, we significantly improve the quality of template-based shape fitting by endowing the SMAL parametric model with Continuous Surface Embeddings, which brings image-to-mesh reprojection constaints that are denser, and thus stronger, than the previously used sparse semantic keypoint correspondences. To model appearance, we propose an implicit duplex-mesh texture that is defined in the canonical pose, but can be deformed using SMAL pose coefficients and later rendered to enforce a photometric compatibility with the input video frames. On the challenging CoP3D and APTv2 datasets, we demonstrate superior results (both in terms of pose estimates and predicted appearance) to existing template-free (RAC) and template-based approaches (BARC, BITE).

Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos

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Generic cuts: an efficient algorithm for optimal inference in higher order MRF-MAP

Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos