25 citations · 50 across the 14 of their papers we have counts for
11 papers · 1 filter
Query-Time Calibration of CLIP Features for Open-Vocabulary 3D Segmentation
Dominik Engel, Yousef Rajeh, Leon Sick +1
Lifting vision-language features such as CLIP into 3D Gaussian reconstructions enables open-vocabulary segmentation through natural-language queries. Although CLIP represents a bro…
EfficientMonoHair: Fast Strand-Level Reconstruction from Monocular Video via Multi-View Direction Fusion
Da Li, Dominik Engel, Deng Luo +1
Strand-level hair geometry reconstruction is a fundamental problem in virtual human modeling and the digitization of hairstyles. However, existing methods still suffer from a signi…
ClickAIXR: On-Device Multimodal Vision-Language Interaction with Real-World Objects in Extended Reality
Dawar Khan, Alexandre Kouyoumdjian, Xinyu Liu +3
We present ClickAIXR, a novel on-device framework for multimodal vision-language interaction with objects in extended reality (XR). Unlike prior systems that rely on cloud-based AI…
S2D: Sparse-To-Dense Keymask Distillation for Unsupervised Video Instance Segmentation
Leon Sick, Lukas Hoyer, Dominik Engel +2
In recent years, the state-of-the-art in unsupervised video instance segmentation has heavily relied on synthetic video data, generated from object-centric image datasets such as I…
CutS3D: Cutting Semantics in 3D for 2D Unsupervised Instance Segmentation
Leon Sick, Dominik Engel, Sebastian Hartwig +2
Traditionally, algorithms that learn to segment object instances in 2D images have heavily relied on large amounts of human-annotated data. Only recently, novel approaches have eme…
A Survey on Quality Metrics for Text-to-Image Generation
Sebastian Hartwig, Dominik Engel, Leon Sick +6
AI-based text-to-image models do not only excel at generating realistic images, they also give designers more and more fine-grained control over the image content. Consequently, th…
Attention-Guided Masked Autoencoders For Learning Image Representations
Leon Sick, Dominik Engel, Pedro Hermosilla +1
Masked autoencoders (MAEs) have established themselves as a powerful method for unsupervised pre-training for computer vision tasks. While vanilla MAEs put equal emphasis on recons…
Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
Leon Sick, Dominik Engel, Pedro Hermosilla +1
Traditionally, training neural networks to perform semantic segmentation required expensive human-made annotations. But more recently, advances in the field of unsupervised learnin…
Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design
Dominik Engel, Leon Sick, Timo Ropinski
In volume rendering, transfer functions are used to classify structures of interest, and to assign optical properties such as color and opacity. They are commonly defined as 1D or…
Monocular Depth Decomposition of Semi-Transparent Volume Renderings
Dominik Engel, Sebastian Hartwig, Timo Ropinski
Neural networks have shown great success in extracting geometric information from color images. Especially, monocular depth estimation networks are increasingly reliable in real-wo…
Deep Volumetric Ambient Occlusion
Dominik Engel, Timo Ropinski
We present a novel deep learning based technique for volumetric ambient occlusion in the context of direct volume rendering. Our proposed Deep Volumetric Ambient Occlusion (DVAO) a…