Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Idanwo Iduro Eniyan

Review 2D pose keypoints, top-down and bottom-up pipelines, Part Affinity Fields and what image-plane estimates cannot establish.

Jẹmọ itọsọna awọn ọnaIdiyele Iduro Eniyan
Ibeere 1 ti 8

Kini iṣiro iduro eniyan ni akọkọ ṣe awari?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini iṣiro iduro eniyan ni akọkọ ṣe awari?
    • Awọn ipo ti awọn isẹpo ara tabi awọn bọtini bọtini
    • Awọn awọ ti aṣọ
    • Orukọ eniyan naa
    • Iwọn faili aworan naa
  2. Bawo ni awọn ọna iṣiro iduro oke-isalẹ ṣiṣẹ?
    • Wọn ṣe awari gbogbo awọn isẹpo ni ẹẹkan, lẹhinna ṣajọpọ wọn
    • Wọn ṣawari ẹni kọọkan ni akọkọ, lẹhinna wa awọn isẹpo inu apoti
    • Wọn kò lo bounding apoti
    • Wọn ṣiṣẹ nikan lori isẹpo aimi kan ṣoṣo
  3. What does OpenPose predict to estimate body-part locations?
    • A direct 3D skeleton with depth
    • A single bounding box per frame
    • Confidence maps for candidate body-part locations
    • A sequence of spoken joint names
  4. What step does the OpenPose parser use to associate candidate body parts into full poses?
    • A face-recognition classifier
    • A top-down detector for every limb
    • A language-model prompt
    • A set of bipartite matchings scored with Part Affinity Fields
  5. What failure mode of top-down pose estimation does the OpenPose paper discuss in crowded scenes?
    • A person detector may miss people close together, and the pose stage cannot recover them
    • It always detects more people than are present
    • It cannot estimate any 2D keypoints
    • It requires a 3D depth camera
  6. Which additional keypoint groups did the OpenPose paper include in its system?
    • Only facial landmarks
    • Body, foot, hand and facial keypoints
    • A 3D skeleton with medical diagnoses
    • Only fingertip positions
  7. What information do Part Affinity Fields encode to connect keypoints?
    • A person’s identity from face recognition
    • Depth in meters for every body joint
    • Vectors indicating limb position and orientation across the image
    • The camera exposure settings
  8. What can a 2D pose model conclude directly from image keypoints?
    • A person’s medical diagnosis
    • Exact real-world joint depth without additional information
    • The person’s intentions
    • Approximate joint locations in image coordinates