17 citations · 133 across the 50 of their papers we have counts for
8 papers · 1 filter
iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning
Tom Fischer, Yaoyao Liu, Artur Jesslen +6
Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have rec…
Unsupervised Learning of Category-Level 3D Pose from Object-Centric Videos
Leonhard Sommer, Artur Jesslen, Eddy Ilg +1
Category-level 3D pose estimation is a fundamentally important problem in computer vision and robotics, e.g. for embodied agents or to train 3D generative models. However, so far m…
ImageNet3D: Towards General-Purpose Object-Level 3D Understanding
Wufei Ma, Guanning Zeng, Guofeng Zhang +5
A vision model with general-purpose object-level 3D understanding should be capable of inferring both 2D (e.g., class name and bounding box) and 3D information (e.g., 3D location a…
DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data
Qihao Liu, Yi Zhang, Song Bai +2
We present DIRECT-3D, a diffusion-based 3D generative model for creating high-quality 3D assets (represented by Neural Radiance Fields) from text prompts. Unlike recent 3D generati…
Compositional 4D Dynamic Scenes Understanding with Physics Priors for Video Question Answering
Xingrui Wang, Wufei Ma, Angtian Wang +3
For vision-language models (VLMs), understanding the dynamic properties of objects and their interactions in 3D scenes from videos is crucial for effective reasoning about high-lev…
Learning a Category-level Object Pose Estimator without Pose Annotations
Fengrui Tian, Yaoyao Liu, Adam Kortylewski +4
3D object pose estimation is a challenging task. Previous works always require thousands of object images with annotated poses for learning the 3D pose correspondence, which is lab…
A Bayesian Approach to OOD Robustness in Image Classification
Prakhar Kaushik, Adam Kortylewski, Alan Yuille
An important and unsolved problem in computer vision is to ensure that the algorithms are robust to changes in image domains. We address this problem in the scenario where we have…
Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation
Prakhar Kaushik, Aayush Mishra, Adam Kortylewski +1
We consider the problem of source-free unsupervised category-level pose estimation from only RGB images to a target domain without any access to source domain data or 3D annotation…