activity
20142023
most citedLearning a Hierarchical Compositional Shape Vocabulary for Multi-class Object Representation

20 citations · 32 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV2023

Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations

Thomas Tanay, Aleš Leonardis, Matteo Maggioni

While current multi-frame restoration methods combine information from multiple input images using 2D alignment techniques, recent advances in novel view synthesis are paving the w…

cs.CV2023

Tunable Convolutions with Parametric Multi-Loss Optimization

Matteo Maggioni, Thomas Tanay, Francesca Babiloni +2

Behavior of neural networks is irremediably determined by the specific loss and data used during training. However it is often desirable to tune the model at inference time based o…

cs.CV20232 cited

HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation

Linfang Zheng, Chen Wang, Yinghan Sun +5

In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation. 3D graph convolution (3D-GC)…

cs.CV2023

On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks

HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai +10

Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks…

cs.CV2022

Disentangling 3D Attributes from a Single 2D Image: Human Pose, Shape and Garment

Xue Hu, Xinghui Li, Benjamin Busam +3

For visual manipulation tasks, we aim to represent image content with semantically meaningful features. However, learning implicit representations from images often lacks interpret…

cs.CV2022

Prompting for Multi-Modal Tracking

Jinyu Yang, Zhe Li, Feng Zheng +2

Multi-modal tracking gains attention due to its ability to be more accurate and robust in complex scenarios compared to traditional RGB-based tracking. Its key lies in how to fuse…