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Perceptual 3D Simulation With Physical World Modeling
Wanhee Lee, Klemen Kotar, Rahul Mysore Venkatesh +2
Predicting how a scene will evolve after a desired 3D transformation from images is a central goal in vision, graphics, and robotics. Yet unlike ideal simulators with full access t…
Physical Object Understanding with a Physically Controllable World Model
Rahul Venkatesh, Klemen Kotar, Lilian Naing Chen +9
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. Solvi…
Unified 3D Scene Understanding Through Physical World Modeling
Wanhee Lee, Klemen Kotar, Rahul Mysore Venkatesh +4
Understanding 3D scenes requires flexible combinations of visual reasoning tasks, including depth estimation, novel view synthesis, and object manipulation, all of which are essent…
Autoregressive Flow Matching for Motion Prediction
Johnathan Xie, Stefan Stojanov, Cristobal Eyzaguirre +2
Motion prediction has been studied in different contexts with models trained on narrow distributions and applied to downstream tasks in human motion prediction and robotics. Simult…
World Modeling with Probabilistic Structure Integration
Klemen Kotar, Wanhee Lee, Rahul Venkatesh +13
We present Probabilistic Structure Integration (PSI), a system for learning richly controllable and flexibly promptable world models from data. PSI consists of a three-step cycle.…
Weakly-Supervised Learning of Dense Functional Correspondences
Stefan Stojanov, Linan Zhao, Yunzhi Zhang +2
Establishing dense correspondences across image pairs is essential for tasks such as shape reconstruction and robot manipulation. In the challenging setting of matching across diff…
Discovering and using Spelke segments
Rahul Venkatesh, Klemen Kotar, Lilian Naing Chen +10
Segments in computer vision are often defined by semantic considerations and are highly dependent on category-specific conventions. In contrast, developmental psychology suggests t…
Taming generative video models for zero-shot optical flow extraction
Seungwoo Kim, Khai Loong Aw, Klemen Kotar +8
Extracting optical flow from videos remains a core computer vision problem. Motivated by the recent success of large general-purpose models, we ask whether frozen self-supervised v…
3D Scene Understanding Through Local Random Access Sequence Modeling
Wanhee Lee, Klemen Kotar, Rahul Mysore Venkatesh +4
3D scene understanding from single images is a pivotal problem in computer vision with numerous downstream applications in graphics, augmented reality, and robotics. While diffusio…
Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals
Stefan Stojanov, David Wendt, Seungwoo Kim +4
Estimating motion in videos is an essential computer vision problem with many downstream applications, including controllable video generation and robotics. Current solutions are p…
Understanding Physical Dynamics with Counterfactual World Modeling
Rahul Venkatesh, Honglin Chen, Kevin Feigelis +9
The ability to understand physical dynamics is critical for agents to act in the world. Here, we use Counterfactual World Modeling (CWM) to extract vision structures for dynamics u…
Are These the Same Apple? Comparing Images Based on Object Intrinsics
Klemen Kotar, Stephen Tian, Hong-Xing Yu +2
The human visual system can effortlessly recognize an object under different extrinsic factors such as lighting, object poses, and background, yet current computer vision systems o…