Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE International Conference on Computer Vision (ICCV2013) |
| Editors | Kyros Kutulakos, Philip Torr, Steve Seitz, Yi Ma |
| Place of Publication | Piscataway, USA |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 1888-1895 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781479928408 |
| ISBN (Print) | 9781479930227 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | IEEE International Conference on Computer Vision (ICCV2013) - Sydney, Sydney, Australia Duration: 1 Dec 2013 → 8 Dec 2013 |
Conference
| Conference | IEEE International Conference on Computer Vision (ICCV2013) |
|---|---|
| Country | Australia |
| City | Sydney |
| Period | 1/12/13 → 8/12/13 |
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Monocular Image 3D Human Pose Estimation under Self-Occlusion. / Radwan, Ibrahim; Dhall, Abhinav; GOECKE, Roland.
Proceedings of the IEEE International Conference on Computer Vision (ICCV2013). ed. / Kyros Kutulakos; Philip Torr; Steve Seitz; Yi Ma. Piscataway, USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 1888-1895.Research output: A Conference proceeding or a Chapter in Book › Conference contribution
TY - GEN
T1 - Monocular Image 3D Human Pose Estimation under Self-Occlusion
AU - Radwan, Ibrahim
AU - Dhall, Abhinav
AU - GOECKE, Roland
PY - 2013
Y1 - 2013
N2 - In this paper, an automatic approach for 3D pose reconstruction from a single image is proposed. The presence of human body articulation, hallucinated parts and cluttered background leads to ambiguity during the pose inference, which makes the problem non-trivial. Researchers have explored various methods based on motion and shading in order to reduce the ambiguity and reconstruct the 3D pose. The key idea of our algorithm is to impose two types of constraints: kinematic constraints and orientation constraints. The kinematic constraints are imposed by projecting a 3D model onto the input image and pruning of the parts, which are incompatible with the anthropomorphism. The orientation constraint is applied by creating synthetic views via regressing the input view to multiple oriented views. After applying the constraints, the 3D model is projected onto the initial and synthetic views, which further reduces the ambiguity. Finally, we borrow the direction of the unambiguous parts from the synthetic views to the initial one, which results in the 3D pose. Quantitative experiments are performed on the HumanEva-I dataset and qualitatively on unconstrained images from the Image Parse dataset. The results show the robustness of the proposed approach to accurately reconstruct the 3D pose form a single image.
AB - In this paper, an automatic approach for 3D pose reconstruction from a single image is proposed. The presence of human body articulation, hallucinated parts and cluttered background leads to ambiguity during the pose inference, which makes the problem non-trivial. Researchers have explored various methods based on motion and shading in order to reduce the ambiguity and reconstruct the 3D pose. The key idea of our algorithm is to impose two types of constraints: kinematic constraints and orientation constraints. The kinematic constraints are imposed by projecting a 3D model onto the input image and pruning of the parts, which are incompatible with the anthropomorphism. The orientation constraint is applied by creating synthetic views via regressing the input view to multiple oriented views. After applying the constraints, the 3D model is projected onto the initial and synthetic views, which further reduces the ambiguity. Finally, we borrow the direction of the unambiguous parts from the synthetic views to the initial one, which results in the 3D pose. Quantitative experiments are performed on the HumanEva-I dataset and qualitatively on unconstrained images from the Image Parse dataset. The results show the robustness of the proposed approach to accurately reconstruct the 3D pose form a single image.
KW - 3D Pose estimation
KW - Self-occlusion
KW - Mixture of Pictorial Structures
U2 - 10.1109/ICCV.2013.237
DO - 10.1109/ICCV.2013.237
M3 - Conference contribution
SN - 9781479930227
SP - 1888
EP - 1895
BT - Proceedings of the IEEE International Conference on Computer Vision (ICCV2013)
A2 - Kutulakos, Kyros
A2 - Torr, Philip
A2 - Seitz, Steve
A2 - Ma, Yi
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Piscataway, USA
ER -