Athanasios Vlontzos, Bernhard Kainz, Simon Ellershaw, Luca Schmidtke, Anna Lukens, Tomoki Arichi
We lifted 9 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lschmidtke/shape_templates | canonical | 7 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| compute_anchor_loss | Ran | lschmidtke/shape_templates/src/core/utils/losses.py code served (permissive licence) · get_code("3e915aa903575ba6") |
| compute_boundary_loss | Ran | lschmidtke/shape_templates/src/core/utils/losses.py code served (permissive licence) · get_code("3fa27226f242b41d") |
| draw_shape | Ran | lschmidtke/shape_templates/src/core/utils/helper.py code served (permissive licence) · get_code("174942967930e188") |
| get_3rd_point | Ran | lschmidtke/shape_templates/src/core/utils/transforms.py code served (permissive licence) · get_code("9084d28f30c5495b") |
| get_bbx | Ran | lschmidtke/shape_templates/processing/h36m_processing.py code served (permissive licence) · get_code("beb11c9b064fb863") |
| get_dir | Ran | lschmidtke/shape_templates/src/core/utils/transforms.py code served (permissive licence) · get_code("5d546e1ecbee1866") |
| id_generator | Ran | lschmidtke/shape_templates/processing/h36m_processing.py code served (permissive licence) · get_code("a389d0fb57120bdf") |
| load_config | Not yet run | lschmidtke/shape_templates/src/core/utils/helper.py code served (permissive licence) · get_code("a5ac42bbf3a2026c") |
| transform_anchor_points | Not yet run | lschmidtke/shape_templates/src/core/utils/transforms.py code served (permissive licence) · get_code("89dda06edbba1976") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. In the medical context, the latter may be an important biomarker for neurological impairments in infants. Whilst many methods exist, their application has been limited by the need for well annotated large datasets and the inability to generalize to humans of different shapes and body compositions, e.g. children and infants. In this paper we present a novel method for learning pose estimators for human adults and infants in an unsupervised fashion. We approach this as a learnable template matching problem facilitated by deep feature extractors. Human-interpretable landmarks are estimated by transforming a template consisting of predefined body parts that are characterized by 2D Gaussian distributions. Enforcing a connectivity prior guides our model to meaningful human shape representations. We demonstrate the effectiveness of our approach on two different datasets including adults and infants. Project
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2105.04154")
get_code_for_paper("2105.04154")
have("2105.04154")
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