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Found 26 papers, 9 papers with code
The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects
We introduce the ObjectFolder Benchmark, a benchmark suite of 10 tasks for multisensory object-centric learning, centered around object recognition, reconstruction, and manipulation with sight, sound, and touch.
3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes
In this paper, we present a framework capable of learning 3D-grounded visual intuitive physics models from videos of complex scenes with fluids.
Partial-View Object View Synthesis via Filtered Inversion
At inference, given one or more views of a novel real-world object, FINV first finds a set of latent codes for the object by inverting the generative model from multiple initial seeds.
Multi-Object Manipulation via Object-Centric Neural Scattering Functions
Learned visual dynamics models have proven effective for robotic manipulation tasks.
Planning with Spatial-Temporal Abstraction from Point Clouds for Deformable Object Manipulation
Effective planning of long-horizon deformable object manipulation requires suitable abstractions at both the spatial and temporal levels.
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?
Decentralized learning has been advocated and widely deployed to make efficient use of distributed datasets, with an extensive focus on supervised learning (SL) problems.
Reinforcement Learning with Neural Radiance Fields
This paper demonstrates that learning state representations with supervision from Neural Radiance Fields (NeRFs) can improve the performance of RL compared to other learned representations or even low-dimensional, hand-engineered state information.
RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks
Our learned model-based planning framework is comparable to and sometimes better than human subjects on the tested tasks.
ComPhy: Compositional Physical Reasoning of Objects and Events from Videos
In this paper, we take an initial step to highlight the importance of inferring the hidden physical properties not directly observable from visual appearances, by introducing the Compositional Physical Reasoning (ComPhy) dataset.
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
We consider the problem of sequential robotic manipulation of deformable objects using tools.
Learning Multi-Object Dynamics with Compositional Neural Radiance Fields
We present a method to learn compositional multi-object dynamics models from image observations based on implicit object encoders, Neural Radiance Fields (NeRFs), and graph neural networks.
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing
This work takes a step on dynamics modeling in hand-object interactions from dense tactile sensing, which opens the door for future applications in activity learning, human-computer interactions, and imitation learning for robotics.
3D Neural Scene Representations for Visuomotor Control
Humans have a strong intuitive understanding of the 3D environment around us.
Intelligent Carpet: Inferring 3D Human Pose From Tactile Signals
In this work, leveraging such tactile interactions, we propose a 3D human pose estimation approach using the pressure maps recorded by a tactile carpet as input.
Causal Discovery in Physical Systems from Videos
We assume access to different configurations and environmental conditions, i. e., data from unknown interventions on the underlying system; thus, we can hope to discover the correct underlying causal graph without explicit interventions.
Learning Physical Graph Representations from Visual Scenes
To overcome these limitations, we introduce the idea of Physical Scene Graphs (PSGs), which represent scenes as hierarchical graphs, with nodes in the hierarchy corresponding intuitively to object parts at different scales, and edges to physical connections between parts.
Visual Grounding of Learned Physical Models
The abilities to perform physical reasoning and to adapt to new environments, while intrinsic to humans, remain challenging to state-of-the-art computational models.
Learning Compositional Koopman Operators for Model-Based Control
Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis.
CLEVRER: CoLlision Events for Video REpresentation and Reasoning
While these models thrive on the perception-based task (descriptive), they perform poorly on the causal tasks (explanatory, predictive and counterfactual), suggesting that a principled approach for causal reasoning should incorporate the capability of both perceiving complex visual and language inputs, and understanding the underlying dynamics and causal relations.
Connecting Touch and Vision via Cross-Modal Prediction
To connect vision and touch, we introduce new tasks of synthesizing plausible tactile signals from visual inputs as well as imagining how we interact with objects given tactile data as input.
Learning the signatures of the human grasp using a scalable tactile glove
Using a low-cost (about US$10) scalable tactile glove sensor array, we record a large-scale tactile dataset with 135, 000 frames, each covering the full hand, while interacting with 26 different objects.
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
In this paper, we propose to learn a particle-based simulator for complex control tasks.
Propagation Networks for Model-Based Control Under Partial Observation
There has been an increasing interest in learning dynamics simulators for model-based control.
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal.
Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning
Dense object detection and temporal tracking are needed across applications domains ranging from people-tracking to analysis of satellite imagery over time.
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
The proposed method addresses two issues in adapting state- of-the-art generic object detection ConvNets (e. g., faster R-CNN) for face detection: (i) One is to eliminate the heuristic design of prede- fined anchor boxes in the region proposals network (RPN) by exploit- ing a 3D mean face model.
Ranked #7 on
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on Annotated Faces in the Wild
