112 citations · 114 across the 10 of their papers we have counts for
15 papers
MMDiff: Extending Diffusion Transformers for Multi-Modal Generation
Yagmur Akarken, Orest Kupyn, Christian Rupprecht
Diffusion transformers have demonstrated remarkable generative capabilities, yet the rich perceptual representations computed across their denoising trajectory are discarded once t…
FLAT: Feedforward Latent Triangle Splatting for Geometrically Accurate Scene Generation
Orest Kupyn, Goutam Bhat, Philipp Henzler +3
Generating explorable 3D scenes from a single image requires strong generative priors and accurate geometric representations suitable for downstream use. Current video diffusion mo…
PoseDreamer: Scalable and Photorealistic Human Data Generation Pipeline with Diffusion Models
Lorenza Prospero, Orest Kupyn, Ostap Viniavskyi +2
Acquiring labeled datasets for 3D human mesh estimation is challenging due to depth ambiguities and the inherent difficulty of annotating 3D geometry from monocular images. Existin…
VGGRPO: Towards World-Consistent Video Generation with 4D Latent Reward
Zhaochong An, Orest Kupyn, Théo Uscidda +5
Large-scale video diffusion models achieve impressive visual quality, yet often fail to preserve geometric consistency. Prior approaches improve consistency either by augmenting th…
Epipolar Geometry Improves Video Generation Models
Orest Kupyn, Théo Uscidda, Marta Tintore Gazulla +3
Video generation models have advanced significantly through the latent diffusion transformers trained with rectified flow techniques. Yet these models still struggle with geometric…
S3OD: Towards Generalizable Salient Object Detection with Synthetic Data
Orest Kupyn, Hirokatsu Kataoka, Christian Rupprecht
Salient object detection exemplifies data-bounded tasks where expensive pixel-precise annotations force separate model training for related subtasks like DIS and HR-SOD. We present…
VGGHeads: 3D Multi Head Alignment with a Large-Scale Synthetic Dataset
Orest Kupyn, Eugene Khvedchenia, Christian Rupprecht
Human head detection, keypoint estimation, and 3D head model fitting are essential tasks with many applications. However, traditional real-world datasets often suffer from bias, pr…
Dataset Enhancement with Instance-Level Augmentations
Orest Kupyn, Christian Rupprecht
We present a method for expanding a dataset by incorporating knowledge from the wide distribution of pre-trained latent diffusion models. Data augmentations typically incorporate i…
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image
Tetiana Martyniuk, Orest Kupyn, Yana Kurliak +3
We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robust model for 3D Dense Head Alignment in the wild. It contains annotations of over 3.5K landmarks that acc…
FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
Vasyl Borsuk, Roman Vei, Orest Kupyn +3
We present FEAR, a family of fast, efficient, accurate, and robust Siamese visual trackers. We present a novel and efficient way to benefit from dual-template representation for ob…
ActGAN: Flexible and Efficient One-shot Face Reenactment
Ivan Kosarevych, Marian Petruk, Markian Kostiv +3
This paper introduces ActGAN - a novel end-to-end generative adversarial network (GAN) for one-shot face reenactment. Given two images, the goal is to transfer the facial expressio…
Fast and Efficient Model for Real-Time Tiger Detection In The Wild
Orest Kupyn, Dmitry Pranchuk
The highest accuracy object detectors to date are based either on a two-stage approach such as Fast R-CNN or one-stage detectors such as Retina-Net or SSD with deep and complex bac…
DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better
Orest Kupyn, Tetiana Martyniuk, Junru Wu +1
We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring effic…
Safe Augmentation: Learning Task-Specific Transformations from Data
Irynei Baran, Orest Kupyn, Arseny Kravchenko
Data augmentation is widely used as a part of the training process applied to deep learning models, especially in the computer vision domain. Currently, common data augmentation te…
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych +2
We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning is based on a conditional GAN and the content loss . DeblurGAN achieves state-of-the art perf…