- 基于参数化模型 (MANO)的手势姿态估计---全面剖析
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
MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand
- MANO hand model in PyTorch (anatomy consistent, anchors, etc)
For example, the original MANO model adopts the same orthogonal basis as the wrist for all of its 16 joints We seek to find a basis whose three axes can describe three independent hand motions that satisfy the hand anatomy
- 基于MANO的3D手部姿态估计方法：3D Hand Shape and Pose from Images in the Wild
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 (β, θ) ，shape参数 β 和pose参数 θ 分别控制手部的形状和姿态：
- A pytorch Implementation of MANO hand model - GitHub
MANO is a differentiable hand model that can map hand pose parameters (joint angles and root location) and shape parameters into a 3D hand mesh The model is very realistic, has low-dimensions, and can fit any human hand
- 数字人基础 | 3D手部参数化模型2017-2023 - 知乎
首先, 让我们回答一个问题, 为什么我们需要用到MANO参数化模型? 其答案也很简单: 结合深度学习和MANO参数化模型, 我们可以仅凭单张手部图像, 回归出其手部对应的2D 3D Pose, 从而可以在诸如 UE5, Unity 等引擎里进行手势的驱动。
- 【亲测免费】 MANO 项目使用教程 - CSDN博客
通过以上步骤，您可以成功安装并运行 MANO 项目，并根据需要进行配置和使用。 【免费下载链接】MANO A PyTorch Implementation of MANO hand model
- MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints
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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