11 citations · 32 across the 23 of their papers we have counts for
14 papers · 1 filter
HuGeDiff: 3D Human Generation via Diffusion with Gaussian Splatting
Maksym Ivashechkin, Oscar Mendez, Richard Bowden
3D human generation is an important problem with a wide range of applications in computer vision and graphics. Despite recent progress in generative AI such as diffusion models or…
SignSplat: Rendering Sign Language via Gaussian Splatting
Maksym Ivashechkin, Oscar Mendez, Richard Bowden
State-of-the-art approaches for conditional human body rendering via Gaussian splatting typically focus on simple body motions captured from many views. This is often in the contex…
HandOcc: NeRF-based Hand Rendering with Occupancy Networks
Maksym Ivashechkin, Oscar Mendez, Richard Bowden
We propose HandOcc, a novel framework for hand rendering based upon occupancy. Popular rendering methods such as NeRF are often combined with parametric meshes to provide deformabl…
Two Hands Are Better Than One: Resolving Hand to Hand Intersections via Occupancy Networks
Maksym Ivashechkin, Oscar Mendez, Richard Bowden
3D hand pose estimation from images has seen considerable interest from the literature, with new methods improving overall 3D accuracy. One current challenge is to address hand-to-…
Improving 3D Pose Estimation for Sign Language
Maksym Ivashechkin, Oscar Mendez, Richard Bowden
This work addresses 3D human pose reconstruction in single images. We present a method that combines Forward Kinematics (FK) with neural networks to ensure a fast and valid predict…
Denoising Diffusion for 3D Hand Pose Estimation from Images
Maksym Ivashechkin, Oscar Mendez, Richard Bowden
Hand pose estimation from a single image has many applications. However, approaches to full 3D body pose estimation are typically trained on day-to-day activities or actions. As su…
Learning Adaptive Neighborhoods for Graph Neural Networks
Avishkar Saha, Oscar Mendez, Chris Russell +1
Graph convolutional networks (GCNs) enable end-to-end learning on graph structured data. However, many works assume a given graph structure. When the input graph is noisy or unavai…
AFT-VO: Asynchronous Fusion Transformers for Multi-View Visual Odometry Estimation
Nimet Kaygusuz, Oscar Mendez, Richard Bowden
Motion estimation approaches typically employ sensor fusion techniques, such as the Kalman Filter, to handle individual sensor failures. More recently, deep learning-based fusion a…
"The Pedestrian next to the Lamppost" Adaptive Object Graphs for Better Instantaneous Mapping
Avishkar Saha, Oscar Mendez, Chris Russell +1
Estimating a semantically segmented bird's-eye-view (BEV) map from a single image has become a popular technique for autonomous control and navigation. However, they show an increa…
Multi-Camera Sensor Fusion for Visual Odometry using Deep Uncertainty Estimation
Nimet Kaygusuz, Oscar Mendez, Richard Bowden
Visual Odometry (VO) estimation is an important source of information for vehicle state estimation and autonomous driving. Recently, deep learning based approaches have begun to ap…
MDN-VO: Estimating Visual Odometry with Confidence
Nimet Kaygusuz, Oscar Mendez, Richard Bowden
Visual Odometry (VO) is used in many applications including robotics and autonomous systems. However, traditional approaches based on feature matching are computationally expensive…
Translating Images into Maps
Avishkar Saha, Oscar Mendez Maldonado, Chris Russell +1
We approach instantaneous mapping, converting images to a top-down view of the world, as a translation problem. We show how a novel form of transformer network can be used to map f…
There and Back Again: Self-supervised Multispectral Correspondence Estimation
Celyn Walters, Oscar Mendez, Mark Johnson +1
Across a wide range of applications, from autonomous vehicles to medical imaging, multi-spectral images provide an opportunity to extract additional information not present in colo…
Localisation via Deep Imagination: learn the features not the map
Jaime Spencer, Oscar Mendez, Richard Bowden +1
How many times does a human have to drive through the same area to become familiar with it? To begin with, we might first build a mental model of our surroundings. Upon revisiting…