The ViP V2.0 models - Ella, Duke, Billie, and Thelonious - consist of simplified CAD files optimized for finite-element modeling in third-party commercial platforms such as ANSYS and CST.
#Body poser full
To take full advantage of this resolution and realism, our collaborators at ZMT Zurich MedTech have empowered these models with state-of-the-art physics solvers (electromagnetic, thermal, acoustic, and computational fluid dynamics) and tissue models, thereby overcoming the challenges encountered during model manipulation in existing commercial platforms. The ViP3.1 models elevate computational simulations in 3D anatomies to an unprecedented level of detail and accuracy with more than 300 tissues and organs per model and a resolution of 0.5 × 0.5 × 0.5 mm³ throughout the entire body. The newest generation of our phantoms is the ViP3.1. Since their inception, the ViP models have become the gold standard for in silico biophysical modeling applications. Pattern Recognition, pages 5693–5703, 2019.The Virtual Population (ViP) models are a set of detailed high-resolution anatomical models created from magnetic resonance image data of volunteers. In Proceedings of the IEEE Conference on Computer Vision and Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang.ĭeep high-resolution representation learning for human pose In European conference on computer vision, pages 483–499. Stacked hourglass networks for human pose estimation. In European conference on computer vision, pages 740–755.Īlejandro Newell, Kaiyu Yang, and Jia Deng. Microsoft coco: Common objects in context. Ramanan, Piotr Dollár, and C Lawrence Zitnick. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Proceedings of the IEEE Conference on Computer Vision and Pifpaf: Composite fields for human pose estimation. Sven Kreiss, Lorenzo Bertoni, and Alexandre Alahi. Real-time facial surface geometry from monocular video on mobile Yury Kartynnik, Artsiom Ablavatski, Ivan Grishchenko, and Matthias Grundmann. IEEE transactions on pattern analysis and machine intelligence , Openpose: Realtime multi-person 2d pose estimation using part Z Cao, G Martinez Hidalgo, T Simon, SE Wei, and YA Sheikh. On-device, real-time hand tracking with mediapipe. Valentin Bazarevsky, Yury Kartynnik, Andrey Vakunov, Karthik Raveendran, andīlazeface: Sub-millisecond neural face detection on mobile gpus. Our approach natively scales to a bigger number of keypoints, 3D support, and additional keypoint attributes, since it is not based on heatmaps/offset maps and therefore does not require an additional full-resolution layer per each new feature type. ModelĠ.4 1 1 1Desktop CPU with 20 cores (Intel i9-7900X)ġ0 2 2 2Pixel 2 Single Core via XNNPACK backend To verify the human baseline, we asked two annotators to re-annotate the AR dataset independently and obtained an average of 97.2. As an evaluation metric, we use the Percent of Correct Points with 20% tolerance (where we assume the point to be detected correctly if the 2D Euclidean error is smaller than 20% of the corresponding person’s torso size).
Topology with 17 points for evaluation, which is a common subset of both OpenPose and BlazePose. The first dataset, referred to as AR dataset, consist of a wide variety of human poses in the wild, while the second is comprised of yoga/fitness poses only. To that end, we manually annotated two in-house datasets of 1000 images, each with 1–2 people in the scene. To evaluate our model’s quality, we chose OpenPose as a baseline. Vitruvian man aligned via our detector vs. To make such a person detector fast and lightweight, we make the strong, yet for AR applications valid, assumption that the head of the person should always be visible for our single-person use case. We observed that in many cases, the strongest signal to the neural network about the position of the torso is the person’s face (as it has high-contrast features and has fewer variations in appearance). To overcome this limitation, we focus on detecting the bounding box of a relatively rigid body part like the human face or torso. This is because multiple, ambiguous boxes satisfy the intersection over union (IoU) threshold for the NMS algorithm. However, this algorithm breaks down for scenarios that include highly articulated poses like those of humans, e.g. This works well for rigid objects with few degrees of freedom. The majority of modern object detection solutions rely on the Non-Maximum Suppression (NMS) algorithm for their last post-processing step.
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