7 citations · 9 across the 11 of their papers we have counts for
14 papers
BackTranslation2.0 -- A Linguistically Motivated Metric to Assess Sign Language Production
Oliver Cory, Maksym Ivashechkin, Karahan Sahin +6
Sign Languages (SLs) are the primary means of communication for millions of deaf individuals, yet existing evaluation metrics for generated SL remain simplistic and poorly aligned…
SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning
Jianhe Low, Alexandre Symeonidis-Herzig, Maksym Ivashechkin +2
Sign Language Production (SLP) faces a fundamental trade-off: direct text-to-pose models suffer from regression-to-the-mean effects, while dictionary-retrieval methods produce disj…
Meaningful Pose-Based Sign Language Evaluation
Zifan Jiang, Colin Leong, Amit Moryossef +8
We present a comprehensive study on meaningfully evaluating sign language utterances in the form of human skeletal poses. The study covers keypoint distance-based, embedding-based,…
Using Sign Language Production as Data Augmentation to enhance Sign Language Translation
Harry Walsh, Maksym Ivashechkin, Richard Bowden
Machine learning models fundamentally rely on large quantities of high-quality data. Collecting the necessary data for these models can be challenging due to cost, scarcity, and pr…
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…
VSAC: Efficient and Accurate Estimator for H and F
Maksym Ivashechkin, Daniel Barath, Jiri Matas
We present VSAC, a RANSAC-type robust estimator with a number of novelties. It benefits from the introduction of the concept of independent inliers that improves significantly the…
USACv20: robust essential, fundamental and homography matrix estimation
Maksym Ivashechkin, Daniel Barath, Jiri Matas
We review the most recent RANSAC-like hypothesize-and-verify robust estimators. The best performing ones are combined to create a state-of-the-art version of the Universal Sample C…
MAGSAC++, a fast, reliable and accurate robust estimator
Daniel Barath, Jana Noskova, Maksym Ivashechkin +1
A new method for robust estimation, MAGSAC++, is proposed. It introduces a new model quality (scoring) function that does not require the inlier-outlier decision, and a novel margi…
Progressive NAPSAC: sampling from gradually growing neighborhoods
Daniel Barath, Maksym Ivashechkin, Jiri Matas
We propose Progressive NAPSAC, P-NAPSAC in short, which merges the advantages of local and global sampling by drawing samples from gradually growing neighborhoods. Exploiting the f…