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PoseScript: Linking 3D Human Poses and Natural Language by Delmas, Ginger; Weinzaepfel, Philippe; Lucas, Thomas; Moreno-Noguer, Francesc; Rogez, Grégory is a scholarly article available to read on EtoBox.

What is PoseScript: Linking 3D Human Poses and Natural Language about?

Natural language plays a critical role in many computer vision applications, such as image captioning, visual question answering, and cross-modal retrieval, to provide fine-grained semantic information. Unfortunately, while human pose is key to human understanding, current 3D human pose datasets lack detailed language descriptions. To address this issue, we have introduced the PoseScript dataset. This dataset pairs more than six thousand 3D human poses from AMASS with rich human-annotated descriptions of the body parts and their spatial relationships. Additionally, to increase the size of the dataset to a scale that is compatible with data-hungry learning algorithms, we have proposed an elaborate captioning process that generates automatic synthetic descriptions in natural language from given 3D keypoints. This process extracts low-level pose information, known as "posecodes", using a set of simple but generic rules on the 3D keypoints. These posecodes are then combined into higher level textual descriptions using syntactic rules. With automatic annotations, the amount of available data significantly scales up (100k), making it possible to effectively pretrain deep models for finet

Author

Delmas, Ginger; Weinzaepfel, Philippe; Lucas, Thomas; Moreno-Noguer, Francesc; Rogez, Grégory

Published

2022

Language

EN