23 citations · 53 across the 8 of their papers we have counts for
9 papers
Magenta Green Screen: Spectrally Multiplexed Alpha Matting with Deep Colorization
Dmitriy Smirnov, Chloe LeGendre, Xueming Yu +1
We introduce Magenta Green Screen, a novel machine learning--enabled matting technique for recording the color image of a foreground actor and a simultaneous high-quality alpha cha…
Wassersplines for Neural Vector Field--Controlled Animation
Paul Zhang, Dmitriy Smirnov, Justin Solomon
Much of computer-generated animation is created by manipulating meshes with rigs. While this approach works well for animating articulated objects like animals, it has limited flex…
DeepCurrents: Learning Implicit Representations of Shapes with Boundaries
David Palmer, Dmitriy Smirnov, Stephanie Wang +2
Recent techniques have been successful in reconstructing surfaces as level sets of learned functions (such as signed distance fields) parameterized by deep neural networks. Many of…
Interactive All-Hex Meshing via Cuboid Decomposition
Lingxiao Li, Paul Zhang, Dmitriy Smirnov +2
Standard PolyCube-based hexahedral (hex) meshing methods aim to deform the input domain into an axis-aligned PolyCube volume with integer corners; if this deformation is bijective,…
HodgeNet: Learning Spectral Geometry on Triangle Meshes
Dmitriy Smirnov, Justin Solomon
Constrained by the limitations of learning toolkits engineered for other applications, such as those in image processing, many mesh-based learning algorithms employ data flows that…
MarioNette: Self-Supervised Sprite Learning
Dmitriy Smirnov, Michael Gharbi, Matthew Fisher +3
Artists and video game designers often construct 2D animations using libraries of sprites -- textured patches of objects and characters. We propose a deep learning approach that de…
Polygonal Building Segmentation by Frame Field Learning
Nicolas Girard, Dmitriy Smirnov, Justin Solomon +1
While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To h…
Learning Manifold Patch-Based Representations of Man-Made Shapes
Dmitriy Smirnov, Mikhail Bessmeltsev, Justin Solomon
Choosing the right representation for geometry is crucial for making 3D models compatible with existing applications. Focusing on piecewise-smooth man-made shapes, we propose a new…
Deep Parametric Shape Predictions using Distance Fields
Dmitriy Smirnov, Matthew Fisher, Vladimir G. Kim +2
Many tasks in graphics and vision demand machinery for converting shapes into consistent representations with sparse sets of parameters; these representations facilitate rendering,…