17 citations · 133 across the 50 of their papers we have counts for
50 papers
CRONOS: Benchmarking Counterfactual Physical Consistency in Video Models
León Begiristain, Olaf Dünkel, Adam Kortylewski
Video prediction is increasingly viewed as a path toward generalizable world models, yet it remains unclear whether these systems learn underlying causal structure or merely exploi…
Interpretable 3D Neural Object Volumes for Robust Conceptual Reasoning
Nhi Pham, Artur Jesslen, Bernt Schiele +2
With the rise of deep neural networks, especially in safety-critical applications, robustness and interpretability are crucial to ensure their trustworthiness. Recent advances in 3…
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…
Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning
Ruxiao Duan, Jieneng Chen, Adam Kortylewski +2
Replay-based methods in class-incremental learning (CIL) have attained remarkable success. Despite their effectiveness, the inherent memory restriction results in saving a limited…
3D-Aware Visual Question Answering about Parts, Poses and Occlusions
Xingrui Wang, Wufei Ma, Zhuowan Li +2
Despite rapid progress in Visual question answering (VQA), existing datasets and models mainly focus on testing reasoning in 2D. However, it is important that VQA models also under…
3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation
Yi Zhang, Pengliang Ji, Angtian Wang +3
Regression-based methods for 3D human pose estimation directly predict the 3D pose parameters from a 2D image using deep networks. While achieving state-of-the-art performance on s…
Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape
Jiacong Xu, Yi Zhang, Jiawei Peng +17
Accurately estimating the 3D pose and shape is an essential step towards understanding animal behavior, and can potentially benefit many downstream applications, such as wildlife c…
Generating Images with 3D Annotations Using Diffusion Models
Wufei Ma, Qihao Liu, Jiahao Wang +11
Diffusion models have emerged as a powerful generative method, capable of producing stunning photo-realistic images from natural language descriptions. However, these models lack e…
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search
Qihao Liu, Adam Kortylewski, Yutong Bai +2
Text-guided diffusion models (TDMs) are widely applied but can fail unexpectedly. Common failures include: (i) natural-looking text prompts generating images with the wrong content…
Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis
Angtian Wang, Wufei Ma, Alan Yuille +1
Human vision demonstrates higher robustness than current AI algorithms under out-of-distribution scenarios. It has been conjectured such robustness benefits from performing analysi…
Robust Category-Level 3D Pose Estimation from Synthetic Data
Jiahao Yang, Wufei Ma, Angtian Wang +3
Obtaining accurate 3D object poses is vital for numerous computer vision applications, such as 3D reconstruction and scene understanding. However, annotating real-world objects is…
NOVUM: Neural Object Volumes for Robust Object Classification
Artur Jesslen, Guofeng Zhang, Angtian Wang +3
Discriminative models for object classification typically learn image-based representations that do not capture the compositional and 3D nature of objects. In this work, we show th…
OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
Bingchen Zhao, Jiahao Wang, Wufei Ma +7
Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on syntheti…
PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation
Qihao Liu, Adam Kortylewski, Alan Yuille
Human pose and shape (HPS) estimation methods achieve remarkable results. However, current HPS benchmarks are mostly designed to test models in scenarios that are similar to the tr…
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
Zhuowan Li, Xingrui Wang, Elias Stengel-Eskin +4
Visual Question Answering (VQA) models often perform poorly on out-of-distribution data and struggle on domain generalization. Due to the multi-modal nature of this task, multiple…
Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features
Wufei Ma, Angtian Wang, Alan Yuille +1
We consider the problem of category-level 6D pose estimation from a single RGB image. Our approach represents an object category as a cuboid mesh and learns a generative model of t…
VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis
Angtian Wang, Peng Wang, Jian Sun +2
The Gaussian reconstruction kernels have been proposed by Westover (1990) and studied by the computer graphics community back in the 90s, which gives an alternative representation…