Datasets:
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"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."
}
]
}
|