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Found 11 papers, 4 papers with code
3D View Prediction Models of the Dorsal Visual Stream
Deep neural network representations align well with brain activity in the ventral visual stream.
Physion: Evaluating Physical Prediction from Vision in Humans and Machines
While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments.
3D-OES: Viewpoint-Invariant Object-Factorized Environment Simulators
Object motion predictions are computed by a graph neural network that operates over the object features extracted from the 3D neural scene representation.
3D Object Recognition By Corresponding and Quantizing Neural 3D Scene Representations
We can compare the 3D feature maps of two objects by searching alignment across scales and 3D rotations, and, as a result of the operation, we can estimate pose and scale changes without the need for 3D pose annotations.
Embodied Language Grounding with 3D Visual Feature Representations
We propose associating language utterances to 3D visual abstractions of the scene they describe.
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping
Predictive coding theories suggest that the brain learns by predicting observations at various levels of abstraction.
Learning Spatial Common Sense with Geometry-Aware Recurrent Networks
The proposed networks learn to "lift" and integrate 2D visual features over time into latent 3D feature maps of the scene.
Reward Learning from Narrated Demonstrations
Humans effortlessly "program" one another by communicating goals and desires in natural language.
Self-supervised Learning of Motion Capture
In this work, we propose a learning based motion capture model for single camera input.
Ranked #2 on
3D Human Reconstruction
on Surreal
Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision
Researchers have developed excellent feed-forward models that learn to map images to desired outputs, such as to the images' latent factors, or to other images, using supervised learning.
Spectral Methods for Nonparametric Models
Nonparametric models are versatile, albeit computationally expensive, tool for modeling mixture models.
