Search Results for author:
Found 22 papers, 5 papers with code
Neural Dense Non-Rigid Structure from Motion with Latent Space Constraints
We introduce the first dense neural non-rigid structure from motion (N-NRSfM) approach, which can be trained end-to-end in an unsupervised manner from 2D point tracks.
No Bells, Just Whistles: Sports Field Registration by Leveraging Geometric Properties
Broadcast sports field registration is traditionally addressed as a homography estimation task, mapping the visible image area to a planar field model, predominantly focusing on the main camera shot.
Morphological Symmetries in Robotics
We present a comprehensive framework for studying and leveraging morphological symmetries in robotic systems.
VQ-HPS: Human Pose and Shape Estimation in a Vector-Quantized Latent Space
Instead of predicting body model parameters or 3D vertex coordinates, our focus is on forecasting the proposed discrete latent representation, which can be decoded into a registered human mesh.
Robust Wind Turbine Blade Segmentation from RGB Images in the Wild
With the relentless growth of the wind industry, there is an imperious need to design automatic data-driven solutions for wind turbine maintenance.
On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis
We present a comprehensive study on discrete morphological symmetries of dynamical systems, which are commonly observed in biological and artificial locomoting systems, such as legged, swimming, and flying animals/robots/virtual characters.
Permutation-Invariant Relational Network for Multi-person 3D Pose Estimation
For this purpose, we build a residual-like permutation-invariant network that successfully refines potentially corrupted initial 3D poses estimated by an off-the-shelf detector.
3D Multi-Person Pose Estimation (absolute)
3D Multi-Person Pose Estimation (root-relative)
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Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification
For this purpose, our method learns a distribution over all possible radiance fields modelling which is used to quantify the uncertainty associated with the modelled scene.
Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images
We address the problem of multi-person 3D body pose and shape estimation from a single image.
An Adaptable Approach to Learn Realistic Legged Locomotion without Examples
We present experimental results showing that even in a model-free setup and with a simple reactive control architecture, the learned policies can generate realistic and energy-efficient locomotion gaits for a bipedal and a quadrupedal robot.
Grasp-Oriented Fine-grained Cloth Segmentation without Real Supervision
We show that training our network solely with synthetic data and the proposed DA yields results competitive with models trained on real data.
Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations
In this context, we propose Stochastic Neural Radiance Fields (S-NeRF), a generalization of standard NeRF that learns a probability distribution over all the possible radiance fields modeling the scene.
Uncertainty-Aware Camera Pose Estimation from Points and Lines
The new PnP(L) methods outperform the state-of-the-art on real data in isolation, showing an increase in mean translation accuracy by 18% on a representative subset of KITTI, while the new uncertain refinement improves pose accuracy for most of the solvers, e. g. decreasing mean translation error for the EPnP by 16% compared to the standard refinement on the same dataset.
Generating Attribution Maps with Disentangled Masked Backpropagation
Attribution map visualization has arisen as one of the most effective techniques to understand the underlying inference process of Convolutional Neural Networks.
Geometry-Aware Network for Non-Rigid Shape Prediction from a Single View
We propose a method for predicting the 3D shape of a deformable surface from a single view.
Unsupervised Person Image Synthesis in Arbitrary Poses
Given an input image of a person and a desired pose represented by a 2D skeleton, our model renders the image of the same person under the new pose, synthesizing novel views of the parts visible in the input image and hallucinating those that are not seen.
GANimation: Anatomically-aware Facial Animation from a Single Image
Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis.
Image Collection Pop-Up: 3D Reconstruction and Clustering of Rigid and Non-Rigid Categories
This paper introduces an approach to simultaneously estimate 3D shape, camera pose, and object and type of deformation clustering, from partial 2D annotations in a multi-instance collection of images.
DUST: Dual Union of Spatio-Temporal Subspaces for Monocular Multiple Object 3D Reconstruction
We present an approach to reconstruct the 3D shape of multiple deforming objects from incomplete 2D trajectories acquired by a single camera.
Learning Shape, Motion and Elastic Models in Force Space
In this paper, we address the problem of simultaneously recovering the 3D shape and pose of a deformable and potentially elastic object from 2D motion.
Simultaneous Pose and Non-Rigid Shape With Particle Dynamics
In this paper, we propose a sequential solution to simultaneously estimate camera pose and non-rigid 3D shape from a monocular video.
Good Vibrations: A Modal Analysis Approach for Sequential Non-Rigid Structure from Motion
We propose an online solution to non-rigid structure from motion that performs camera pose and 3D shape estimation of highly deformable surfaces on a frame-by-frame basis.
