Search Results for author:
Found 33 papers, 14 papers with code
HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion
To close the gap to production-level quality, we introduce HumanRF, a 4D dynamic neural scene representation that captures full-body appearance in motion from multi-view video input, and enables playback from novel, unseen viewpoints.
Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM
We present Co-SLAM, a neural RGB-D SLAM system based on a hybrid representation, that performs robust camera tracking and high-fidelity surface reconstruction in real time.
Learning Neural Parametric Head Models
We propose a novel 3D morphable model for complete human heads based on hybrid neural fields.
GNPM: Geometric-Aware Neural Parametric Models
We propose Geometric Neural Parametric Models (GNPM), a learned parametric model that takes into account the local structure of data to learn disentangled shape and pose latent spaces of 4D dynamics, using a geometric-aware architecture on point clouds.
One-Shot Transfer of Affordance Regions? AffCorrs!
In this work, we tackle one-shot visual search of object parts.
Ranked #1 on
One-Shot Part Segmentation of Support Affordance - Intra Class
on UMD-i Affrodance Dataset
One-Shot Instance Segmentation
One-Shot Part Segmentation of Contain Affordance - Inter Class
+14
GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface Reconstruction
We present GO-Surf, a direct feature grid optimization method for accurate and fast surface reconstruction from RGB-D sequences.
Bimodal Camera Pose Prediction for Endoscopy
Deducing the 3D structure of endoscopic scenes from images remains extremely challenging.
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings
Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream applications in robotics.
CodeNeRF: Disentangled Neural Radiance Fields for Object Categories
At test time, given a single unposed image of an unseen object, CodeNeRF jointly estimates camera viewpoint, and shape and appearance codes via optimization.
DSP-SLAM: Object Oriented SLAM with Deep Shape Priors
We propose DSP-SLAM, an object-oriented SLAM system that builds a rich and accurate joint map of dense 3D models for foreground objects, and sparse landmark points to represent the background.
Multi-person Implicit Reconstruction from a Single Image
We present a new end-to-end learning framework to obtain detailed and spatially coherent reconstructions of multiple people from a single image.
SelfPose: 3D Egocentric Pose Estimation from a Headset Mounted Camera
The quantitative evaluation, on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric approaches.
S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency
In this work we advocate semantic 3D keypoints as a visual representation, and present a semi-supervised training objective that can allow instance or category-level keypoints to be trained to 1-5 millimeter-accuracy with minimal supervision.
DiverseNet: When One Right Answer is not Enough
For a single input, we learn to predict a range of possible answers.
FroDO: From Detections to 3D Objects
We introduce FroDO, a method for accurate 3D reconstruction of object instances from RGB video that infers object location, pose and shape in a coarse-to-fine manner.
xR-EgoPose: Egocentric 3D Human Pose from an HMD Camera
Our quantitative evaluation, both on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric pose estimation approaches.
3D Pick & Mix: Object Part Blending in Joint Shape and Image Manifolds
We present 3D Pick & Mix, a new 3D shape retrieval system that provides users with a new level of freedom to explore 3D shape and Internet image collections by introducing the ability to reason about objects at the level of their constituent parts.
Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture
We propose a CNN-based approach for multi-camera markerless motion capture of the human body.
Ranked #169 on
3D Human Pose Estimation
on Human3.6M
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects
We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D SLAM system that goes beyond traditional systems which output a purely geometric map of a static scene.
Training VAEs Under Structured Residuals
This paper demonstrates a novel scheme to incorporate a structured Gaussian likelihood prediction network within the VAE that allows the residual correlations to be modeled.
Structured Uncertainty Prediction Networks
This paper is the first work to propose a network to predict a structured uncertainty distribution for a synthesized image.
Better Together: Joint Reasoning for Non-rigid 3D Reconstruction with Specularities and Shading
We demonstrate the use of shape-from-shading (SfS) to improve both the quality and the robustness of 3D reconstruction of dynamic objects captured by a single camera.
Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or semantic cues) while simultaneously tracking and reconstructing their 3D shape in real time.
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks.
Ranked #20 on
Weakly-supervised 3D Human Pose Estimation
on Human3.6M
Direct, Dense, and Deformable: Template-Based Non-Rigid 3D Reconstruction From RGB Video
In contrast, our method makes use of a single RGB video as input; it can capture the deformations of generic shapes; and the depth estimation is dense, per-pixel and direct.
Solving Jigsaw Puzzles with Linear Programming
We propose a novel Linear Program (LP) based formula- tion for solving jigsaw puzzles.
Part-Based Modelling of Compound Scenes From Images
We propose a method to recover the structure of a compound scene from multiple silhouettes.
Lifting Object Detection Datasets into 3D
In particular, acquiring ground truth 3D shapes of objects pictured in 2D images remains a challenging feat and this has hampered progress in recognition-based object reconstruction from a single image.
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.
Reconstructing PASCAL VOC
We address the problem of populating object category detection datasets with dense, per-object 3D reconstructions, bootstrapped from class labels, ground truth figure-ground segmentations and a small set of keypoint annotations.
Looking Beyond the Image: Unsupervised Learning for Object Saliency and Detection
We propose a principled probabilistic formulation of object saliency as a sampling problem.
Learning a Manifold as an Atlas
In this work, we return to the underlying mathematical definition of a manifold and directly characterise learning a manifold as finding an atlas, or a set of overlapping charts, that accurately describe local structure.
Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video
This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence.
