{
  "field_id": "F001",
  "field": "3d human pose estimation",
  "directions": [
    {
      "direction_id": "G0",
      "name": "Articulated human recovery from visual observations",
      "scope": "Infer 3D human joints, pose, motion, or body shape from 2D pose, images, video, or contemporaneous multi-view observations; this also includes candidate poses and probabilistic priors under ambiguity."
    },
    {
      "direction_id": "G1",
      "name": "Context-constrained human reconstruction",
      "scope": "When relationships between the human body and other people, the camera, the environment, or unseen body regions determine whether a reconstruction is credible, jointly infer the human body and its location, motion, or constrained hidden parts."
    },
    {
      "direction_id": "G2",
      "name": "Body recovery from partial and specialized measurements",
      "scope": "Recover full-body pose, motion, or shape from self-mounted observations that do not directly show the complete body, or from directly acquired 3D point measurements that may be sparse or incomplete."
    },
    {
      "direction_id": "G3",
      "name": "Training evidence and transferable representations for human recovery",
      "scope": "When direct 3D annotations are limited, training coverage is insufficient, or downstream tasks require transferable representations, construct supervision, training examples, or pretrained representations to support pose and body recovery."
    },
    {
      "direction_id": "G4",
      "name": "Human reconstruction under computational constraints",
      "scope": "Maintain usable 3D pose or mesh reconstruction quality while reducing model size, inference computation, or device burden."
    }
  ]
}