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Daniel Bear

7 accepted papers

2026

Physical Object Understanding with a Physically Controllable World Model

CVPR 2026

A central challenge in visual intelligence is learning the physical structure of scenes from raw videos: how regions form objects and the laws that govern their interactions. Solving these tasks requires world models capable of inferring distributional states of the world from partial observations -

Cited by 0SourceScholar
2021

Physion: Evaluating Physical Prediction from Vision in Humans and Machines

NeurIPS 2021poster

While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments. Here we introduce Physion, a dataset and benchmark for rigorously evaluating the ability to predict how physical scenarios will evolve over time.…

Cited by 82SourcecodeScholar
2021

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation

NeurIPS 2021poster

We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables the simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties include real-time near-photo-realistic image ren…

Cited by 342SourcecodeScholar
2020

Learning Physical Graph Representations from Visual Scenes

NeurIPS 2020oral

Convolutional Neural Networks (CNNs) have proved exceptional at learning representations for visual object categorization. However, CNNs do not explicitly encode objects, parts, and their physical properties, which has limited CNNs' success on tasks that require structured understanding of visual sc…

Cited by 98SourcePDFScholar
2020

Visual Grounding of Learned Physical Models

ICML 2020poster

Humans intuitively recognize objects’ physical properties and predict their motion, even when the objects are engaged in complicated interactions. The abilities to perform physical reasoning and to adapt to new environments, while intrinsic to humans, remain challenging to state-of-the-art computati…

Cited by 82SourcePDFScholar
2019

Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

NeurIPS 2019oral

Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially inspired by brain anatomy, over the past years, these ANNs have evolved from a simple eight-layer architecture in AlexN…

2018

Task-Driven Convolutional Recurrent Models of the Visual System

NeurIPS 2018poster

Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object classification tasks such as ImageNet. Further, they are quantitatively accurate models of temporally-averaged responses of neurons in the primate brain's visual system. However, biological visual systems ha…