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

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1. Proche 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…

2. Proche 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 …

3. Apparenté Pas pertinent

   Dynamic Multi-Person Mesh Recovery From Uncalibrated Multi-View Cameras

   Buzhen Huang, Yuan Shu, Tianshu Zhang et al. · 2021 · Vision par ordinateur et reconnaissance de formes · Résumé IA disponible

   Résumé scientifique

   Dynamic multi-person mesh recovery has been a hot topic in 3D vision recently. However, few works focus on the multi-person motion capture from uncalibrated cameras, which mainly faces two challenges: the one is that inter-person interactions and occlusions introduce inherent ambiguities for both camera calibration and motion capture; The other is that a lack of dense correspondences can be used to constrain sparse camera geometries in a dynamic multi-person scene. Our key idea is incorporating motion prior knowledge into simultaneous optimization of extrinsic camera parameters and human meshes from noisy human semantics. First, we introduce a physics-geometry consistency to reduce the low a…

4. Apparenté Pas pertinent

   DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion

   Cédric Rommel, Eduardo Valle, Mickaël Chen et al. · 2023 · Vision par ordinateur et reconnaissance de formes · Pas encore résumé

   Résumé scientifique

   We present an innovative approach to 3D Human Pose Estimation (3D-HPE) by integrating cutting-edge diffusion models, which have revolutionized diverse fields, but are relatively unexplored in 3D-HPE. We show that diffusion models enhance the accuracy, robustness, and coherence of human pose estimations. We introduce DiffHPE, a novel strategy for harnessing diffusion models in 3D-HPE, and demonstrate its ability to refine standard supervised 3D-HPE. We also show how diffusion models lead to more robust estimations in the face of occlusions, and improve the time-coherence and the sagittal symmetry of predictions. Using the Human\,3.6M dataset, we illustrate the effectiveness of our approach an…

5. Apparenté Pas pertinent

   ParaHome: Parameterizing Everyday Home Activities Towards 3D Generative Modeling of Human-Object Interactions

   Jeonghwan Kim, Jisoo Kim, Jeonghyeon Na et al. · 2024 · Vision par ordinateur et reconnaissance de formes · Résumé IA disponible

   Résumé scientifique

   To enable machines to learn how humans interact with the physical world in our daily activities, it is crucial to provide rich data that encompasses the 3D motion of humans as well as the motion of objects in a learnable 3D representation. Ideally, this data should be collected in a natural setup, capturing the authentic dynamic 3D signals during human-object interactions. To address this challenge, we introduce the ParaHome system, designed to capture and parameterize dynamic 3D movements of humans and objects within a common home environment. Our system consists of a multi-view setup with 70 synchronized RGB cameras, as well as wearable motion capture devices equipped with an IMU-based bod…

6. Apparenté Pas pertinent

   3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera

   Christen Millerdurai, Diogo Luvizon, Viktor Rudnev et al. · 2023 · International Conference on 3D Vision (3DV) 2024 · Résumé IA disponible

   Résumé scientifique

   3D hand tracking from a monocular video is a very challenging problem due to hand interactions, occlusions, left-right hand ambiguity, and fast motion. Most existing methods rely on RGB inputs, which have severe limitations under low-light conditions and suffer from motion blur. In contrast, event cameras capture local brightness changes instead of full image frames and do not suffer from the described effects. Unfortunately, existing image-based techniques cannot be directly applied to events due to significant differences in the data modalities. In response to these challenges, this paper introduces the first framework for 3D tracking of two fast-moving and interacting hands from a single …

7. 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…

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…