Search Results for author:
Found 8 papers, 5 papers with code
Towards Precise Completion of Deformable Shapes
Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.
PhysGraph: Physics-Based Integration Using Graph Neural Networks
Our contribution is based on a simple observation: evaluating forces is computationally relatively cheap for traditional simulation methods and can be computed in parallel in contrast to their integration.
FETA: Towards Specializing Foundation Models for Expert Task Applications
However, as we show in this paper, FMs still have poor out-of-the-box performance on expert tasks (e. g. retrieval of car manuals technical illustrations from language queries), data for which is either unseen or belonging to a long-tail part of the data distribution of the huge datasets used for FM pre-training.
Ranked #1 on
Image-to-Text Retrieval
on FETA Car-Manuals
Garment Avatars: Realistic Cloth Driving using Pattern Registration
Here, we propose an end-to-end pipeline for building drivable representations for clothing.
LIMP: Learning Latent Shape Representations with Metric Preservation Priors
In this paper, we advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D shapes.
The Whole Is Greater Than the Sum of Its Nonrigid Parts
Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.
Unsupervised Learning of Dense Shape Correspondence
The resulting learning model is class-agnostic, and is able to leverage any type of deformable geometric data for the training phase.
Self-supervised Learning of Dense Shape Correspondence
The resulting learning model is class-agnostic, and is able to leverage any type of deformable geometric data for the training phase.
