Recurrent human pose estimation
We propose a ConvNet model for predicting 2D human body poses in an image. The model regresses a heatmap representation for each body keypoint, and is able to learn and represent both the part appearances and the context of the part configuration. We make the following three contributions: (i) an ar...
| Главные авторы: | , |
|---|---|
| Формат: | Conference item |
| Опубликовано: |
Institute of Electrical and Electronics Engineers
2017
|
| _version_ | 1826304613446320128 |
|---|---|
| author | Belagiannis, V Zisserman, A |
| author_facet | Belagiannis, V Zisserman, A |
| author_sort | Belagiannis, V |
| collection | OXFORD |
| description | We propose a ConvNet model for predicting 2D human body poses in an image. The model regresses a heatmap representation for each body keypoint, and is able to learn and represent both the part appearances and the context of the part configuration. We make the following three contributions: (i) an architecture combining a feed forward module with a recurrent module, where the recurrent module can be run iteratively to improve the performance; (ii) the model can be trained end-to-end and from scratch, with auxiliary losses incorporated to improve performance; (iii) we investigate whether keypoint visibility can also be predicted. The model is evaluated on two benchmark datasets. The result is a simple architecture that achieves performance on par with the state of the art, but without the complexity of a graphical model stage (or layers). |
| first_indexed | 2024-03-07T06:20:29Z |
| format | Conference item |
| id | oxford-uuid:f28ba2c9-b4fb-41b6-9927-79f38b4ce388 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:20:29Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:f28ba2c9-b4fb-41b6-9927-79f38b4ce3882022-03-27T12:04:41ZRecurrent human pose estimationConference itemhttp://purl.org/coar/resource_type/c_5794uuid:f28ba2c9-b4fb-41b6-9927-79f38b4ce388Symplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2017Belagiannis, VZisserman, AWe propose a ConvNet model for predicting 2D human body poses in an image. The model regresses a heatmap representation for each body keypoint, and is able to learn and represent both the part appearances and the context of the part configuration. We make the following three contributions: (i) an architecture combining a feed forward module with a recurrent module, where the recurrent module can be run iteratively to improve the performance; (ii) the model can be trained end-to-end and from scratch, with auxiliary losses incorporated to improve performance; (iii) we investigate whether keypoint visibility can also be predicted. The model is evaluated on two benchmark datasets. The result is a simple architecture that achieves performance on par with the state of the art, but without the complexity of a graphical model stage (or layers). |
| spellingShingle | Belagiannis, V Zisserman, A Recurrent human pose estimation |
| title | Recurrent human pose estimation |
| title_full | Recurrent human pose estimation |
| title_fullStr | Recurrent human pose estimation |
| title_full_unstemmed | Recurrent human pose estimation |
| title_short | Recurrent human pose estimation |
| title_sort | recurrent human pose estimation |
| work_keys_str_mv | AT belagiannisv recurrenthumanposeestimation AT zissermana recurrenthumanposeestimation |