6. • ちなみに:Honorable MentionはGroup NormalizationとGANimation
GANimation: Anatomically-aware Facial Animation from a Single Image
Albert Pumarola, Antonio Agudo, Aleix M. Martinez, Alberto Sanfeliu, Francesc Moreno-Noguer
6
ECCV 2018
7. • Single- / Multi- Person
• Multi-Person: Top-Down / Bottom-Up
– Top-Down: Person Detection → Single-Person Pose Estimation x N
• High Accuracy but Slow. Dependent to Person Detector Performance.
– Bottom-Up: Joint Candidate Detection → Grouping
• Fast but Low Accuracy. Complex Partitioning.
• Single Image / Sequential Images
– Temporal Coherence
• With / Without Depth
• 2D / 3D Pose Estimation
• Supervised / Semi-Supervised / Unsupervised
7
Human Pose Estimationのトレンド・キーワード
8. 8
Human Pose Estimation @ ECCV2018
Multi-Person
• Pose Proposal Networks [Sekii]
• Pose Partition Networks for Multi-Person Pose Estimation [Nie+]
• MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network [Kocabas+]
• PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up,
Part-Based, Geometric Embedding Model [Papandreou+]
(Mainly)
Single-Person
• Deeply Learned Compositional Models for Human Pose Estimation [Tang+]
• Learning 3D Huma Pose from Structure and Motion [Dabral+]
• Exploiting temporal information for 3D human pose estimation [Hossain+]
• Integral Human Pose Regression [Sun+]
• Multi-Scale Structure-Aware Network for Human Pose Estimation [Sun+]
• Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation [Rhodin+]
• 3D Ego-Pose Estimation via Imitation Learning [Yuan+]
• Recovering Accurate 3D Human Pose inThe Wild Using IMUs and a Moving Camera [Marcard+]
• Deformable PoseTraversal Convolution for 3D Action and Gesture Recognition [Weng+]
2D
2D
2D
2D
3D
3D
3D
3D
3D
3D
2D
3D
2D
9. 9
Human Pose Estimation @ ECCV2018
Multi-Person
• Pose Proposal Networks [Sekii]
• Pose Partition Networks for Multi-Person Pose Estimation [Nie+]
• MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network [Kocabas+]
• PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up,
Part-Based, Geometric Embedding Model [Papandreou+]
(Mainly)
Single-Person
• Deeply Learned Compositional Models for Human Pose Estimation [Tang+]
• Learning 3D Huma Pose from Structure and Motion [Dabral+]
• Exploiting temporal information for 3D human pose estimation [Hossain+]
• Integral Human Pose Regression [Sun+]
• Multi-Scale Structure-Aware Network for Human Pose Estimation [Sun+]
• Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation [Rhodin+]
• 3D Ego-Pose Estimation via Imitation Learning [Yuan+]
• Recovering Accurate 3D Human Pose inThe Wild Using IMUs and a Moving Camera [Marcard+]
• Deformable PoseTraversal Convolution for 3D Action and Gesture Recognition [Weng+]
2D
2D
2D
2D
3D
3D
3D
3D
3D
3D
2D
3D
2D
14. • Pose EstimationとInstance Segmentationを同時に行うBottom-up式の手法の提案
• 人物のkeypointsを検出した後に相互の関係性を推論し、人物ごとのposeにgroupingする
• keypointのlocalizationの精度向上や接続を推論するためにoffsetという概念を導入
14
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up,
Part-Based, Geometric Embedding Model [Papandreou+]
15. • Pose EstimationとInstance Segmentationを同時に行うBottom-up式の手法の提案
• 人物のkeypointsを検出した後に相互の関係性を推論し、人物ごとのposeにgroupingする
• keypointのlocalizationの精度向上や接続を推論するためにoffsetという概念を導入
• 推論時間の記述はなし
15
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up,
Part-Based, Geometric Embedding Model [Papandreou+]
16. • Bottom-up式の2D Multi-Person Pose Estimation
• Person Detection, Person Segmentation, Pose EstimationのMulti-Task Learning
16
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network [Kocabas+]
17. • Bottom-up式の2D Multi-Person Pose Estimation
• Person Detection, Person Segmentation, Pose EstimationのMulti-Task Learning
• PersonLabより高精度(AP)、推論速度は27FPS (1 person) ~ 15FPS (20 person)、
COCOで23FPS (~3 person)
17
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network [Kocabas+]
18. 18
Human Pose Estimation @ ECCV2018
Multi-Person
• Pose Proposal Networks [Sekii]
• Pose Partition Networks for Multi-Person Pose Estimation [Nie+]
• MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network [Kocabas+]
• PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up,
Part-Based, Geometric Embedding Model [Papandreou+]
(Mainly)
Single-Person
• Deeply Learned Compositional Models for Human Pose Estimation [Tang+]
• Learning 3D Huma Pose from Structure and Motion [Dabral+]
• Exploiting temporal information for 3D human pose estimation [Hossain+]
• Integral Human Pose Regression [Sun+]
• Multi-Scale Structure-Aware Network for Human Pose Estimation [Sun+]
• Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation [Rhodin+]
• 3D Ego-Pose Estimation via Imitation Learning [Yuan+]
• Recovering Accurate 3D Human Pose inThe Wild Using IMUs and a Moving Camera [Marcard+]
• Deformable PoseTraversal Convolution for 3D Action and Gesture Recognition [Weng+]
2D
2D
2D
2D
3D
3D
3D
3D
3D
3D
2D
3D
2D