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08/28/2023
NSF: Neural Surface Fields for Human Modeling from Monocular Depth
Obtaining personalized 3D animatable avatars from a monocular camera has...
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06/01/2023
Object pop-up: Can we infer 3D objects and their poses from human interactions alone?
The intimate entanglement between objects affordances and human poses is...
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05/17/2023
Accelerating Transformer Inference for Translation via Parallel Decoding
Autoregressive decoding limits the efficiency of transformers for Machin...
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11/26/2022
Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching
In this work we present a novel approach for computing correspondences b...
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06/08/2022
Metric Based Few-Shot Graph Classification
Many modern deep-learning techniques do not work without enormous datase...
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05/30/2022
Harnessing spectral representations for subgraph alignment
With the rise and advent of graph learning techniques, graph data has be...
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12/14/2021
Why you should learn functional basis
Efficient and practical representation of geometric data is a ubiquitous...
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08/26/2021
A functional skeleton transfer
The animation community has spent significant effort trying to ease rigg...
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08/04/2021
Learning to generate shape from global-local spectra
In this work, we present a new learning-based pipeline for the generatio...
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06/25/2021
Shape registration in the time of transformers
In this paper, we propose a transformer-based procedure for the efficien...
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10/25/2020
Correspondence Learning via Linearly-invariant Embedding
In this paper, we propose a fully differentiable pipeline for estimating...
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09/19/2020
High-Resolution Augmentation for Automatic Template-Based Matching of Human Models
We propose a new approach for 3D shape matching of deformable human shap...
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03/14/2020
Instant recovery of shape from spectrum via latent space connections
We introduce the first learning-based method for recovering shapes from ...
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07/27/2018