Image-based Stability Quantification

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1. Apparenté Pas pertinent

   Estimacion de carga muscular mediante imagenes

   Leandro Abraham, Facundo Bromberg, Raymundo Forradellas · 2016 · Vision par ordinateur et reconnaissance de formes · Résumé IA disponible

   Résumé scientifique

   Un problema de gran interes en disciplinas como la ocupacional, ergonomica y deportiva, es la medicion de variables biomecanicas involucradas en el movimiento humano (como las fuerzas musculares internas y torque de articulaciones). Actualmente este problema se resuelve en un proceso de dos pasos. Primero capturando datos con dispositivos poco pr\'acticos, intrusivos y costosos. Luego estos datos son usados como entrada en modelos complejos para obtener las variables biomecanicas como salida. El presente trabajo representa una alternativa automatizada, no intrusiva y economica al primer paso, proponiendo la captura de estos datos a traves de imagenes. En trabajos futuros la idea es automatiz…

2. Apparenté Pas pertinent

   CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction

   Lixin Yang, Xinyu Zhan, Kailin Li et al. · 2020 · Vision par ordinateur et reconnaissance de formes · Résumé IA disponible

   Résumé scientifique

   Modeling the hand-object (HO) interaction not only requires estimation of the HO pose, but also pays attention to the contact due to their interaction. Significant progress has been made in estimating hand and object separately with deep learning methods, simultaneous HO pose estimation and contact modeling has not yet been fully explored. In this paper, we present an explicit contact representation namely Contact Potential Field (CPF), and a learning-fitting hybrid framework namely MIHO to Modeling the Interaction of Hand and Object. In CPF, we treat each contacting HO vertex pair as a spring-mass system. Hence the whole system forms a potential field with minimal elastic energy at the gras…

3. Apparenté Pas pertinent

   LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images

   Gregory Rogez, Philippe Weinzaepfel, Cordelia Schmid · 2018 · Vision par ordinateur et reconnaissance de formes · Résumé IA disponible

   Résumé scientifique

   We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images. Key to our approach is the generation and scoring of a number of pose proposals per image, which allows us to predict 2D and 3D poses of multiple people simultaneously. Hence, our approach does not require an approximate localization of the humans for initialization. Our Localization-Classification-Regression architecture, named LCR-Net, contains 3 main components: 1) the pose proposal generator that suggests candidate poses at different locations in the image; 2) a classifier that scores the different pose proposals; and 3) a regressor that refines pose proposals both in 2D and 3D. All three s…

4. Apparenté Pas pertinent

   OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

   Zhe Cao, Gines Hidalgo, Tomas Simon et al. · 2018 · Vision par ordinateur et reconnaissance de formes · Résumé IA disponible

   Résumé scientifique

   Realtime multi-person 2D pose estimation is a key component in enabling machines to have an understanding of people in images and videos. In this work, we present a realtime approach to detect the 2D pose of multiple people in an image. The proposed method uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. This bottom-up system achieves high accuracy and realtime performance, regardless of the number of people in the image. In previous work, PAFs and body part location estimation were refined simultaneously across training stages. We demonstrate that a PAF-only refinement rather than both PAF …

5. Apparenté Pas pertinent

   Learnable human mesh triangulation for 3D human pose and shape estimation

   Sungho Chun, Sungbum Park, Ju Yong Chang · 2022 · Vision par ordinateur et reconnaissance de formes · Pas encore résumé

   Résumé scientifique

   Compared to joint position, the accuracy of joint rotation and shape estimation has received relatively little attention in the skinned multi-person linear model (SMPL)-based human mesh reconstruction from multi-view images. The work in this field is broadly classified into two categories. The first approach performs joint estimation and then produces SMPL parameters by fitting SMPL to resultant joints. The second approach regresses SMPL parameters directly from the input images through a convolutional neural network (CNN)-based model. However, these approaches suffer from the lack of information for resolving the ambiguity of joint rotation and shape reconstruction and the difficulty of net…

6. Apparenté Pas pertinent

   Accurate 3D Body Shape Regression using Metric and Semantic Attributes

   Vasileios Choutas, Lea Muller, Chun-Hao P. Huang et al. · 2022 · CVPR 2022 · Résumé IA disponible

   Résumé scientifique

   While methods that regress 3D human meshes from images have progressed rapidly, the estimated body shapes often do not capture the true human shape. This is problematic since, for many applications, accurate body shape is as important as pose. The key reason that body shape accuracy lags pose accuracy is the lack of data. While humans can label 2D joints, and these constrain 3D pose, it is not so easy to "label" 3D body shape. Since paired data with images and 3D body shape are rare, we exploit two sources of information: (1) we collect internet images of diverse "fashion" models together with a small set of anthropometric measurements; (2) we collect linguistic shape attributes for a wide r…

7. Apparenté Pas pertinent

   Real-time Recognition of Yoga Poses using computer Vision for Smart Health Care

   Abhishek Sharma, Yash Shah, Yash Agrawal et al. · 2022 · Vision par ordinateur et reconnaissance de formes · Pas encore résumé

   Résumé scientifique

   Nowadays, yoga has become a part of life for many people. Exercises and sports technological assistance is implemented in yoga pose identification. In this work, a self-assistance based yoga posture identification technique is developed, which helps users to perform Yoga with the correction feature in Real-time. The work also presents Yoga-hand mudra (hand gestures) identification. The YOGI dataset has been developed which include 10 Yoga postures with around 400-900 images of each pose and also contain 5 mudras for identification of mudras postures. It contains around 500 images of each mudra. The feature has been extracted by making a skeleton on the body for yoga poses and hand for mudra …

8. Apparenté Pas pertinent

   FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects

   Bowen Wen, Wei Yang, Jan Kautz et al. · 2023 · Vision par ordinateur et reconnaissance de formes · Résumé IA disponible

   Résumé scientifique

   We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups. Our approach can be instantly applied at test-time to a novel object without fine-tuning, as long as its CAD model is given, or a small number of reference images are captured. We bridge the gap between these two setups with a neural implicit representation that allows for effective novel view synthesis, keeping the downstream pose estimation modules invariant under the same unified framework. Strong generalizability is achieved via large-scale synthetic training, aided by a large language model (LLM), a novel transformer-based architecture, and…