Deep Network for Integrated 3D Sensing of Multiple People in Natural Images

Deep Network for Integrated 3D Sensing of Multiple People in Natural Images

UCF CRCV via YouTube Direct link

Qualitative Results

15 of 15

15 of 15

Qualitative Results

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Deep Network for Integrated 3D Sensing of Multiple People in Natural Images

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  1. 1 Problem: Human Localization and Grouping
  2. 2 Objective
  3. 3 Approach: Find the Skeletal Joints
  4. 4 Ground Truth: Panoptic Studio
  5. 5 Computational Pipeline
  6. 6 Deep Volume Encoding (2d/3d)
  7. 7 Limb Scoring
  8. 8 3d Pose Decoding and Shape Estimati
  9. 9 Given the feature volume of a person and it's skeleton
  10. 10 SMPL: A Skinned Multi-Person Linear Model
  11. 11 Deep Autoencoder
  12. 12 Analysis of learning
  13. 13 Quantitative Results
  14. 14 CMU Panoptic dataset
  15. 15 Qualitative Results

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