HumanAI18 CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction, ICCV’21 논문 링크 : https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_CPF_Learning_a_Contact_Potential_Field_To_Model_the_Hand-Object_ICCV_2021_paper.pdf1. IntroductionTo model the contact, we propose an explicit representation named Contact Potential Field (CPF, §4). It is built upon the idea that the contact between a hand and an object mesh under grasp configuration is multi-point contact, which.. 2023. 4. 6. GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes, CVPR’19 논문 링크 : https://openaccess.thecvf.com/content_CVPR_2020/papers/Corona_GanHand_Predicting_Human_Grasp_Affordances_in_Multi-Object_Scenes_CVPR_2020_paper.pdf1. IntroductionIn order to predict feasible human grasps, we introduce GanHand, a multi-task GAN architecture that given solely one input image: 1) estimates the 3D shape/pose of the objects; 2) predicts the best grasp type according to a taxo.. 2023. 4. 6. Learning Actor Relation Graphs for Group Activity Recognition, CVPR’19 논문 링크 : https://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_Learning_Actor_Relation_Graphs_for_Group_Activity_Recognition_CVPR_2019_paper.pdf1. IntroductionTo understand the scene of multiple persons, the model needs to not only describe the individual action of each actor in the context, but also infer their collective activity.However, modeling the relation between actors is challenging,.. 2023. 3. 26. Mesh Graphormer, ICCV’21 논문 링크 : https://openaccess.thecvf.com/content/ICCV2021/papers/Lin_Mesh_Graphormer_ICCV_2021_paper.pdf1. IntroductionTransformers are good at modeling long-range dependencies on the input tokens, but they are less efficient at capturing fine-grained local information.Convolution layers, on the other hand, are useful for extracting local features, but many layers are required to capture global con.. 2023. 3. 26. 이전 1 2 3 4 5 다음