Human Pose, Shape and Action
3D Pose from Images
2D Pose from Images
Beyond Motion Capture
Action and Behavior
Body Perception
Body Applications
Pose and Motion Priors
Clothing Models (2011-2015)
Reflectance Filtering
Learning on Manifolds
Markerless Animal Motion Capture
Multi-Camera Capture
2D Pose from Optical Flow
Body Perception
Neural Prosthetics and Decoding
Part-based Body Models
Intrinsic Depth
Lie Bodies
Layers, Time and Segmentation
Understanding Action Recognition (JHMDB)
Intrinsic Video
Intrinsic Images
Action Recognition with Tracking
Neural Control of Grasping
Flowing Puppets
Faces
Deformable Structures
Model-based Anthropometry
Modeling 3D Human Breathing
Optical flow in the LGN
FlowCap
Smooth Loops from Unconstrained Video
PCA Flow
Efficient and Scalable Inference
Motion Blur in Layers
Facade Segmentation
Smooth Metric Learning
Robust PCA
3D Recognition
Object Detection
Measuring the Cost of Planning with Bayesian Inverse Reinforcement Learning
In this project, we investigate to which extent seemingly irrational planning decisions are a consequence of how people individually experience the costs and benefits of deliberate decision-making. We start from the empirically-grounded assumption that many sub-optimal decisions arise from being short-sighted when balancing the costs and benefits of a particular decision. To achieve this, we leverage Bayesian Inverse Reinforcement Learning [Ramachandran and Amir, IJCAI 2007] to infer experienced reward functions. In future work, we will investigate personalized interventions based on differences in these experienced costs. This work may result in insights about human decision-making, applicable to a wide range of domains such as public policy, psychiatric treatment, and the field of education.
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