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
Computing Optimal Sub-Goals
] based on resource-rational models of human goal-pursuit []. We also evaluated whether a similar method could be used to help people set personal goals [].
Breaking a goal into small, manageable parts can be an effective strategy to help people achieve their goals. However, deciding how to divide a goal into smaller subgoals can be nearly as challenging as achieving the goal directly.
In this project, we investigate how people set subgoals and use a computational model of goal pursuit [See also: computational modeling of goal pursuit] to compute optimal subgoals that help people achieve their goals.
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