![]() In particular, we estimate the scene depth and person scale from normalized disparity predictions using the 2D body joints and joint angles. Thus, we introduce the first non-linear optimization-based approach that jointly solves for the 3D position of each human, their articulated pose, their individual shapes as well as the scale of the scene. To deal with this challenging setting, we leverage recent advances in computer vision using large-scale pre-trained models for a variety of modalities, including 2D body joints, joint angles, normalized disparity maps, and human segmentation masks. In contrast to expensive marker-based or multi-view systems, our lightweight setup is ideal for private users as it enables an affordable 3D motion capture that is easy to install and does not require expert knowledge. ![]() ![]() ![]() In this work, we consider the problem of estimating the 3D position of multiple humans in a scene as well as their body shape and articulation from a single RGB video recorded with a static camera. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |