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Found 6 papers, 1 papers with code
Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals
Estimating motion in videos is an essential computer vision problem with many downstream applications, including controllable video generation and robotics.
Understanding Physical Dynamics with Counterfactual World Modeling
The ability to understand physical dynamics is critical for agents to act in the world.
Unifying (Machine) Vision via Counterfactual World Modeling
Leading approaches in machine vision employ different architectures for different tasks, trained on costly task-specific labeled datasets.
Unsupervised Segmentation in Real-World Images via Spelke Object Inference
Self-supervised, category-agnostic segmentation of real-world images is a challenging open problem in computer vision.
Deep Implicit Surface Point Prediction Networks
Unsigned distance function (UDF) based approaches have been proposed recently as a promising alternative to represent both open and closed shapes.
DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfaces
Several implicit 3D shape representation approaches using deep neural networks have been proposed leading to significant improvements in both quality of representations as well as the impact on downstream applications.
