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  • [2201. 02610] Embodied Hands: Modeling and Capturing Hands and Bodies . . .
    To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations) MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses
  • MANO
    To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations) MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses
  • 基于参数化模型 (MANO)的手势姿态估计---全面剖析 - CSDN博客
    本文深入解析了MANO参数化模型，介绍了其在手部姿态估计中的应用流程，包括数据处理、模型推理及手部解剖学特点。 MANO模型通过处理相机参数、形状和姿态参数，实现了从图像到三维姿态的有效估计。
  • Mano: Restriking Manifold Optimization for LLM Training
    By innovatively projecting the momentum onto the tangent space of model parameters and constraining it on a rotational Oblique manifold, we propose a novel, powerful, and efficient optimizer **Mano** that is the first to bridge the performance gap between manifold optimization and modern optimizers
  • 基于MANO的3D手部姿态估计方法：3D Hand Shape and . . .
    这篇论文使用的手部模型为MANO [2]，它类似于人体模型 SMPL，如果了解过SMPL对于MANO应该很容易理解。 MANO可以表示为函数 M (\beta,\theta) ，shape参数 \beta 和pose参数 \theta 分别控制手部的形状和姿态： W 表示线性混合蒙皮 (LBS)，它根据 K=16 个关节点的位置来牵动手部网格 T 。 J 表示关节点回归函数，可以从mesh中回归出3D joints。 \mathcal {W} 是混合权重blend weights。
  • MANO | Proceedings of the 38th International Conference on Neural . . .
    We conduct an extensive empirical study on common unsupervised accuracy estimation benchmarks and demonstrate that MANO achieves state-of-the-art performance across various architectures in the presence of synthetic, natural, or subpopulation shifts
  • MaNo | Bloggin on Responsible AI
    This is a blog post about the paper MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts, published by Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Weijian Deng, Jianfeng Zhang and Bo An in November 2024 and avalaible on arXiv
  • Mano: Restriking Manifold Optimization for LLM Training - arXiv. org
    In this study, we restriked manifold optimization methods for training LLMs, which may address both optimizers’ limitations, while conventional manifold optimization methods have been largely overlooked due to the poor performance in large-scale model optimization
  • Embodied Hands: Modeling and Capturing Hands and Bodies Together
    To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations) MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses
  • MS-MANO: Enabling Hand Pose Tracking with . . .
    Our evaluation of the accuracy of MS-MANO and the efficacy of the BioPR is conducted in two separate parts The accuracy of MS-MANO is compared with MyoSuite, while the efficacy of BioPR is benchmarked against two large-scale public datasets and two recent state-of-the-art methods





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