Guide-yakabatana mibvunzo · Pakati Nhanho

Human Pose Estimation Quiz

Wongorora 2D pose makiyi, kumusoro-pasi uye kuzasi-kumusoro mapaipi, Chikamu Affinity Minda uye izvo fungidziro yemufananidzo-ndege isingagone kumisa.

Nzira dzekutungamira dzinoenderanaKutsvaga Kureva kweMuviri weMunhu
Mubvunzo 1 ye 8

Ko fungidziro yechimiro chemunhu inonyanyoonei?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Ko fungidziro yechimiro chemunhu inonyanyoonei?
    • Nzvimbo dzemajoini emuviri kana keypoints
    • Mavara ezvipfeko
    • Zita romunhu wacho
    • Saizi yefaira remufananidzo
  2. Nzira dzekufungidzira dzepamusoro-pasi dzinoshanda sei?
    • Vanoona majoini ese kamwechete, vobva vaabatanidza
    • Vanoona munhu wese kutanga, vozowana majoini mukati mebhokisi
    • Havamboshandisi mabhokisi ekusunga
    • Vanongoshanda pane imwechete static joint
  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