2 code implementations • CVPR 2021 • Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, Sanja Fidler
We introduce an efficient neural representation that, for the first time, enables real-time rendering of high-fidelity neural SDFs, while achieving state-of-the-art geometry reconstruction quality.
1 code implementation • NeurIPS 2019 • Wenzheng Chen, Jun Gao, Huan Ling, Edward J. Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler
Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering.
Ranked #4 on Single-View 3D Reconstruction on ShapeNet
1 code implementation • 15 Jun 2022 • Towaki Takikawa, Alex Evans, Jonathan Tremblay, Thomas Müller, Morgan McGuire, Alec Jacobson, Sanja Fidler
Neural approximations of scalar and vector fields, such as signed distance functions and radiance fields, have emerged as accurate, high-quality representations.
1 code implementation • 5 Feb 2022 • Nicholas Sharp, Alec Jacobson
Neural implicit representations, which encode a surface as the level set of a neural network applied to spatial coordinates, have proven to be remarkably effective for optimizing, compressing, and generating 3D geometry.
1 code implementation • NeurIPS 2020 • Jun Gao, Wenzheng Chen, Tommy Xiang, Clement Fuji Tsang, Alec Jacobson, Morgan McGuire, Sanja Fidler
We introduce Deformable Tetrahedral Meshes (DefTet) as a particular parameterization that utilizes volumetric tetrahedral meshes for the reconstruction problem.
1 code implementation • 5 May 2023 • Nicholas Sharp, Cristian Romero, Alec Jacobson, Etienne Vouga, Paul G. Kry, David I. W. Levin, Justin Solomon
Physical systems ranging from elastic bodies to kinematic linkages are defined on high-dimensional configuration spaces, yet their typical low-energy configurations are concentrated on much lower-dimensional subspaces.
2 code implementations • 4 May 2020 • Hsueh-Ti Derek Liu, Vladimir G. Kim, Siddhartha Chaudhuri, Noam Aigerman, Alec Jacobson
During inference, our method takes a coarse triangle mesh as input and recursively subdivides it to a finer geometry by applying the fixed topological updates of Loop Subdivision, but predicting vertex positions using a neural network conditioned on the local geometry of a patch.
1 code implementation • 6 Sep 2023 • Lily Goli, Cody Reading, Silvia Sellán, Alec Jacobson, Andrea Tagliasacchi
Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation, but learning from multiview images faces inherent uncertainties.
1 code implementation • 20 Oct 2022 • Silvia Sellán, Yun-Chun Chen, Ziyi Wu, Animesh Garg, Alec Jacobson
We introduce Breaking Bad, a large-scale dataset of fractured objects.
3 code implementations • 17 Sep 2020 • Thomas Davies, Derek Nowrouzezahrai, Alec Jacobson
Many prior works have focused on _latent-encoded_ neural implicits, where a latent vector encoding of a specific shape is also fed as input.
no code implementations • ICLR 2019 • Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, Alec Jacobson
As such, we propose the direct perturbation of physical parameters that underly image formation: lighting and geometry.
no code implementations • 6 Apr 2020 • Timothy Jeruzalski, David I. W. Levin, Alec Jacobson, Paul Lalonde, Mohammad Norouzi, Andrea Tagliasacchi
In this technical report, we investigate efficient representations of articulated objects (e. g. human bodies), which is an important problem in computer vision and graphics.
no code implementations • 1 Dec 2020 • Jiayi Eris Zhang, Seungbae Bang, David I.W. Levin, Alec Jacobson
Our method does not require a particular type of rig and adds secondary effects to skeletal animations, cage-based deformations, wire deformers, motion capture data, and rigid-body simulations.
no code implementations • 16 Feb 2022 • Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson, Sanja Fidler, Or Litany
The latent descriptor of a neural field acts as a deformation handle for the 3D shape it represents.
no code implementations • 26 May 2022 • Selena Ling, Nicholas Sharp, Alec Jacobson
We demonstrate this approach on problems in machine learning and traditional geometric optimization, showing that equivariant VectorAdam resolves the artifacts and biases of traditional Adam when applied to vector-valued data, with equivalent or even improved rates of convergence.
no code implementations • CVPR 2022 • Yun-Chun Chen, Haoda Li, Dylan Turpin, Alec Jacobson, Animesh Garg
While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we interpret assembly more literally: as mating geometric parts together to achieve a snug fit.
no code implementations • 10 Aug 2023 • Yun-Chun Chen, Vladimir G. Kim, Noam Aigerman, Alec Jacobson
The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e. g., 3D meshes, over the Internet.
no code implementations • 21 Sep 2023 • Silvia Sellán, Alec Jacobson
Reconstructing a surface from a point cloud is an underdetermined problem.
no code implementations • 16 Dec 2023 • Vikas Thamizharasan, Difan Liu, Shantanu Agarwal, Matthew Fisher, Michael Gharbi, Oliver Wang, Alec Jacobson, Evangelos Kalogerakis
We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions.
no code implementations • 28 Dec 2023 • Towaki Takikawa, Thomas Müller, Merlin Nimier-David, Alex Evans, Sanja Fidler, Alec Jacobson, Alexander Keller
Neural graphics primitives are faster and achieve higher quality when their neural networks are augmented by spatial data structures that hold trainable features arranged in a grid.