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07/14/2023
CoTracker: It is Better to Track Together
Methods for video motion prediction either estimate jointly the instanta...
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05/03/2023
DynamicStereo: Consistent Dynamic Depth from Stereo Videos
We consider the problem of reconstructing a dynamic scene observed from ...
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03/21/2023
Real-time volumetric rendering of dynamic humans
We present a method for fast 3D reconstruction and real-time rendering o...
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12/06/2022
Self-Supervised Correspondence Estimation via Multiview Registration
Video provides us with the spatio-temporal consistency needed for visual...
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08/31/2021
DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension
We tackle the problem of monocular 3D reconstruction of articulated obje...
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04/22/2021
Pri3D: Can 3D Priors Help 2D Representation Learning?
Recent advances in 3D perception have shown impressive progress in under...
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12/16/2020
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts
The rapid progress in 3D scene understanding has come with growing deman...
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11/20/2020
RidgeSfM: Structure from Motion via Robust Pairwise Matching Under Depth Uncertainty
We consider the problem of simultaneously estimating a dense depth map a...
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11/02/2020
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
We consider the problem of obtaining dense 3D reconstructions of humans ...
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04/15/2020
Training with Quantization Noise for Extreme Model Compression
We tackle the problem of producing compact models, maximizing their accu...
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04/15/2020
Training with Quantization Noise for Extreme Fixed-Point Compression
We tackle the problem of producing compact models, maximizing their accu...
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09/05/2019
C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion
We propose C3DPO, a method for extracting 3D models of deformable object...
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07/12/2019
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
In this paper, we address the problem of reducing the memory footprint o...
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02/27/2019
Equi-normalization of Neural Networks
Modern neural networks are over-parametrized. In particular, each rectif...
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11/26/2018
Unsupervised learning with sparse space-and-time autoencoders
We use spatially-sparse two, three and four dimensional convolutional au...
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02/22/2018
The iisignature library: efficient calculation of iterated-integral signatures and log signatures
Iterated-integral signatures and log signatures are vectors calculated f...
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11/28/2017
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Convolutional networks are the de-facto standard for analyzing spatio-te...
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10/17/2017
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data a...
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06/05/2017
Submanifold Sparse Convolutional Networks
Convolutional network are the de-facto standard for analysing spatio-tem...
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02/27/2017
Low-Precision Batch-Normalized Activations
Artificial neural networks can be trained with relatively low-precision ...
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10/23/2015
Confusing Deep Convolution Networks by Relabelling
Deep convolutional neural networks have become the gold standard for ima...
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12/18/2014
Fractional Max-Pooling
Convolutional networks almost always incorporate some form of spatial po...
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09/22/2014
Spatially-sparse convolutional neural networks
Convolutional neural networks (CNNs) perform well on problems such as ha...
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08/01/2013