79 citations · 226 across the 27 of their papers we have counts for
7 papers · 1 filter
Learning a Neural 3D Texture Space from 2D Exemplars
Philipp Henzler, Niloy J. Mitra, Tobias Ritschel
We propose a generative model of 2D and 3D natural textures with diversity, visual fidelity and at high computational efficiency. This is enabled by a family of methods that extend…
StructEdit: Learning Structural Shape Variations
Kaichun Mo, Paul Guerrero, Li Yi +4
Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape…
Neural Re-Simulation for Generating Bounces in Single Images
Carlo Innamorati, Bryan Russell, Danny M. Kaufman +1
We introduce a method to generate videos of dynamic virtual objects plausibly interacting via collisions with a still image's environment. Given a starting trajectory, physically s…
StructureNet: Hierarchical Graph Networks for 3D Shape Generation
Kaichun Mo, Paul Guerrero, Li Yi +4
The ability to generate novel, diverse, and realistic 3D shapes along with associated part semantics and structure is central to many applications requiring high-quality 3D assets…
Going Deeper with Lean Point Networks
Eric-Tuan Le, Iasonas Kokkinos, Niloy J. Mitra
In this work we introduce Lean Point Networks (LPNs) to train deeper and more accurate point processing networks by relying on three novel point processing blocks that improve memo…
Unsupervised Intuitive Physics from Past Experiences
Sébastien Ehrhardt, Aron Monszpart, Niloy J. Mitra +1
We are interested in learning models of intuitive physics similar to the ones that animals use for navigation, manipulation and planning. In addition to learning general physical p…