DGPose: Deep Generative Models for Human Body Analysis
Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility. In this work, we present deep generative models for human body analysis in...
| मुख्य लेखकों: | , , , , , , |
|---|---|
| स्वरूप: | Journal article |
| प्रकाशित: |
Springer
2020
|
| _version_ | 1826286541172899840 |
|---|---|
| author | de Bem, R Ghosh, A Ajanthan, T Miksik, O Boukhayma, A Siddharth, N Torr, P |
| author_facet | de Bem, R Ghosh, A Ajanthan, T Miksik, O Boukhayma, A Siddharth, N Torr, P |
| author_sort | de Bem, R |
| collection | OXFORD |
| description | Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However,
the latent space learned by such approaches is typically not interpretable, resulting in less flexibility. In this work, we present
deep generative models for human body analysis in which the body pose and the visual appearance are disentangled. Such
a disentanglement allows independent manipulation of pose and appearance, and hence enables applications such as posetransfer without specific training for such a task. Our proposed models, the Conditional-DGPose and the Semi-DGPose, have
different characteristics. In the first, body pose labels are taken as conditioners, from a fully-supervised training set. In the
second, our structured semi-supervised approach allows for pose estimation to be performed by the model itself and relaxes
the need for labelled data. Therefore, the Semi-DGPose aims for the joint understanding and generation of people in images.
It is not only capable of mapping images to interpretable latent representations but also able to map these representations
back to the image space. We compare our models with relevant baselines, the ClothNet-Body and the Pose Guided Person
Generation networks, demonstrating their merits on the Human3.6M, ChictopiaPlus and DeepFashion benchmarks. |
| first_indexed | 2024-03-07T01:45:18Z |
| format | Journal article |
| id | oxford-uuid:9834505c-1cfb-495f-a46d-2ddc04d64861 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T01:45:18Z |
| publishDate | 2020 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:9834505c-1cfb-495f-a46d-2ddc04d648612022-03-27T00:05:31ZDGPose: Deep Generative Models for Human Body AnalysisJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:9834505c-1cfb-495f-a46d-2ddc04d64861Symplectic ElementsSpringer2020de Bem, RGhosh, AAjanthan, TMiksik, OBoukhayma, ASiddharth, NTorr, PDeep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility. In this work, we present deep generative models for human body analysis in which the body pose and the visual appearance are disentangled. Such a disentanglement allows independent manipulation of pose and appearance, and hence enables applications such as posetransfer without specific training for such a task. Our proposed models, the Conditional-DGPose and the Semi-DGPose, have different characteristics. In the first, body pose labels are taken as conditioners, from a fully-supervised training set. In the second, our structured semi-supervised approach allows for pose estimation to be performed by the model itself and relaxes the need for labelled data. Therefore, the Semi-DGPose aims for the joint understanding and generation of people in images. It is not only capable of mapping images to interpretable latent representations but also able to map these representations back to the image space. We compare our models with relevant baselines, the ClothNet-Body and the Pose Guided Person Generation networks, demonstrating their merits on the Human3.6M, ChictopiaPlus and DeepFashion benchmarks. |
| spellingShingle | de Bem, R Ghosh, A Ajanthan, T Miksik, O Boukhayma, A Siddharth, N Torr, P DGPose: Deep Generative Models for Human Body Analysis |
| title | DGPose: Deep Generative Models for Human Body Analysis |
| title_full | DGPose: Deep Generative Models for Human Body Analysis |
| title_fullStr | DGPose: Deep Generative Models for Human Body Analysis |
| title_full_unstemmed | DGPose: Deep Generative Models for Human Body Analysis |
| title_short | DGPose: Deep Generative Models for Human Body Analysis |
| title_sort | dgpose deep generative models for human body analysis |
| work_keys_str_mv | AT debemr dgposedeepgenerativemodelsforhumanbodyanalysis AT ghosha dgposedeepgenerativemodelsforhumanbodyanalysis AT ajanthant dgposedeepgenerativemodelsforhumanbodyanalysis AT miksiko dgposedeepgenerativemodelsforhumanbodyanalysis AT boukhaymaa dgposedeepgenerativemodelsforhumanbodyanalysis AT siddharthn dgposedeepgenerativemodelsforhumanbodyanalysis AT torrp dgposedeepgenerativemodelsforhumanbodyanalysis |