Search Results for author: Andrea Tagliasacchi

Found 41 papers, 16 papers with code

NASA Neural Articulated Shape Approximation

no code implementations ECCV 2020 Boyang Deng, JP Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, Andrea Tagliasacchi

Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics.

Neural Dual Contouring

1 code implementation4 Feb 2022 Zhiqin Chen, Andrea Tagliasacchi, Thomas Funkhouser, Hao Zhang

We introduce neural dual contouring (NDC), a new data-driven approach to mesh reconstruction based on dual contouring (DC).

Surface Reconstruction

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation

no code implementations9 Dec 2021 Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, Vincent Sitzmann

Our performance generalizes across both object instances and 6-DoF object poses, and significantly outperforms a recent baseline that relies on 2D descriptors.

CoNeRF: Controllable Neural Radiance Fields

no code implementations3 Dec 2021 Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzciński, Andrea Tagliasacchi

We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i. e. camera control).

Few-Shot Learning

Urban Radiance Fields

no code implementations29 Nov 2021 Konstantinos Rematas, Andrew Liu, Pratul P. Srinivasan, Jonathan T. Barron, Andrea Tagliasacchi, Thomas Funkhouser, Vittorio Ferrari

The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments (e. g., Street View).

3D Reconstruction Novel View Synthesis

VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance Field

1 code implementation25 Nov 2021 Naruya Kondo, Yuya Ikeda, Andrea Tagliasacchi, Yutaka Matsuo, Yoichi Ochiai, Shixiang Shane Gu

We hope VaxNeRF -- a careful combination of a classic technique with a deep method (that arguably replaced it) -- can empower and accelerate new NeRF extensions and applications, with its simplicity, portability, and reliable performance gains.

3D Reconstruction Meta-Learning

Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations

no code implementations25 Nov 2021 Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, Jakob Uszkoreit, Thomas Funkhouser, Andrea Tagliasacchi

In this work, we propose the Scene Representation Transformer (SRT), a method which processes posed or unposed RGB images of a new area, infers a "set-latent scene representation", and synthesises novel views, all in a single feed-forward pass.

Novel View Synthesis Semantic Segmentation

LOLNeRF: Learn from One Look

no code implementations19 Nov 2021 Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, Andrea Tagliasacchi

We present a method for learning a generative 3D model based on neural radiance fields, trained solely from data with only single views of each object.

Depth Estimation Novel View Synthesis

MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision

no code implementations10 Aug 2021 Ben Usman, Andrea Tagliasacchi, Kate Saenko, Avneesh Sud

In the era of deep learning, human pose estimation from multiple cameras with unknown calibration has received little attention to date.

Pose Estimation

Learning Mesh Representations via Binary Space Partitioning Tree Networks

1 code implementation27 Jun 2021 Zhiqin Chen, Andrea Tagliasacchi, Hao Zhang

The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built over a set of planes, where the planes and convexes are both defined by learned network weights.

MIST: Multiple Instance Spatial Transformer

1 code implementation CVPR 2021 Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts.

Image Reconstruction

Deep Medial Fields

no code implementations7 Jun 2021 Daniel Rebain, Ke Li, Vincent Sitzmann, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi

Implicit representations of geometry, such as occupancy fields or signed distance fields (SDF), have recently re-gained popularity in encoding 3D solid shape in a functional form.

Vector Neurons: A General Framework for SO(3)-Equivariant Networks

3 code implementations ICCV 2021 Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, Leonidas Guibas

Invariance and equivariance to the rotation group have been widely discussed in the 3D deep learning community for pointclouds.

COTR: Correspondence Transformer for Matching Across Images

1 code implementation ICCV 2021 Wei Jiang, Eduard Trulls, Jan Hosang, Andrea Tagliasacchi, Kwang Moo Yi

We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other.

Dense Pixel Correspondence Estimation Optical Flow Estimation

Human 3D keypoints via spatial uncertainty modeling

no code implementations18 Dec 2020 Francis Williams, Or Litany, Avneesh Sud, Kevin Swersky, Andrea Tagliasacchi

We introduce a technique for 3D human keypoint estimation that directly models the notion of spatial uncertainty of a keypoint.

ShapeFlow: Learnable Deformation Flows Among 3D Shapes

no code implementations NeurIPS 2020 Chiyu Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas J. Guibas

Such a space naturally allows the disentanglement of geometric style (coming from the source) and structural pose (conforming to the target).

Disentanglement Style Transfer

DeRF: Decomposed Radiance Fields

no code implementations CVPR 2021 Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi

Moreover, we show that a Voronoi spatial decomposition is preferable for this purpose, as it is provably compatible with the Painter's Algorithm for efficient and GPU-friendly rendering.

Voronoi Convolutional Neural Networks

no code implementations21 Oct 2020 Soroosh Yazdani, Andrea Tagliasacchi

In this technical report, we investigate extending convolutional neural networks to the setting where functions are not sampled in a grid pattern.

CoSE: Compositional Stroke Embeddings

1 code implementation NeurIPS 2020 Emre Aksan, Thomas Deselaers, Andrea Tagliasacchi, Otmar Hilliges

We demonstrate qualitatively and quantitatively that our proposed approach is able to model the appearance of individual strokes, as well as the compositional structure of larger diagram drawings.

ShapeFlow: Learnable Deformations Among 3D Shapes

1 code implementation14 Jun 2020 Chiyu "Max" Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas Guibas

We illustrate the effectiveness of this learned deformation space for various downstream applications, including shape generation via deformation, geometric style transfer, unsupervised learning of a consistent parameterization for entire classes of shapes, and shape interpolation.

Disentanglement Style Transfer

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.

VoronoiNet: General Functional Approximators with Local Support

no code implementations8 Dec 2019 Francis Williams, Daniele Panozzo, Kwang Moo Yi, Andrea Tagliasacchi

Voronoi diagrams are highly compact representations that are used in various Graphics applications.

NASA: Neural Articulated Shape Approximation

no code implementations6 Dec 2019 Boyang Deng, JP Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, Andrea Tagliasacchi

Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics.

BSP-Net: Generating Compact Meshes via Binary Space Partitioning

3 code implementations CVPR 2020 Zhiqin Chen, Andrea Tagliasacchi, Hao Zhang

The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built on a set of planes.

3D Reconstruction 3D Shape Representation

MIST: Multiple Instance Spatial Transformer Networks

no code implementations25 Sep 2019 Baptiste Angles, Simon Kornblith, Shahram Izadi, Andrea Tagliasacchi, Kwang Moo Yi

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts.

Image Reconstruction

Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning

no code implementations CVPR 2019 Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello

The key insight is to leverage previously seen "calibration" images of a given user to extrapolate what should be rendered in a novel viewpoint from the data available in the sensor.

MIST: Multiple Instance Spatial Transformer Network

1 code implementation26 Nov 2018 Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts.

Anomaly Detection In Surveillance Videos Image Reconstruction

Espresso: Efficient Forward Propagation for Binary Deep Neural Networks

no code implementations ICLR 2018 Fabrizio Pedersoli, George Tzanetakis, Andrea Tagliasacchi

Binary Deep Neural Networks (BDNNs) have been shown to be an effective way of achieving this objective.

Low-Dimensionality Calibration Through Local Anisotropic Scaling for Robust Hand Model Personalization

1 code implementation ICCV 2017 Edoardo Remelli, Anastasia Tkach, Andrea Tagliasacchi, Mark Pauly

We present a robust algorithm for personalizing a sphere-mesh tracking model to a user from a collection of depth measurements.

Espresso: Efficient Forward Propagation for BCNNs

1 code implementation19 May 2017 Fabrizio Pedersoli, George Tzanetakis, Andrea Tagliasacchi

In this paper, we show how Convolutional Neural Networks (CNNs) can be implemented using binary representations.

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