Search Results for author: Alec Jacobson

Found 11 papers, 5 papers with code

Learning Smooth Neural Functions via Lipschitz Regularization

no code implementations16 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.

Spelunking the Deep: Guaranteed Queries for General Neural Implicit Surfaces

no code implementations5 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.

Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors

no code implementations29 Sep 2021 Yun-Chun Chen, Haoda Li, Dylan Turpin, Alec Jacobson, Animesh Garg

To train NSM, we present a self-supervised data collection pipeline that generates pairwise shape assembly data with ground truth by randomly cutting an object mesh into two parts, resulting in a dataset that consists of 19, 226 shape assembly pairs with numerous object meshes and diverse cut types.

Point Cloud Registration

Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes

1 code implementation 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.

Complementary Dynamics

no code implementations1 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.

Learning Deformable Tetrahedral Meshes for 3D Reconstruction

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.

3D Reconstruction

On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes

3 code implementations17 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.

3D Shape Representation

Neural Subdivision

2 code implementations4 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.

NiLBS: Neural Inverse Linear Blend Skinning

no code implementations6 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.

Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer

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.

Single-View 3D Reconstruction

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