论文标题
部分可观测时空混沌系统的无模型预测
LWA-HAND: Lightweight Attention Hand for Interacting Hand Reconstruction
论文作者
论文摘要
近年来，在实时应用程序（例如视觉现实和增强现实）中的手部重建方面取得了巨大的成功，同时通过有效的变压器与双手重建相互作用。在本文中，我们提出了一种称为轻巧注意手（LWA手）的方法，以从单个RGB图像中重建低功能中的手。为了解决有效的注意体系结构中的阻塞和相互作用问题，我们在本文中提出了三个移动注意模块。第一个模块是一个轻巧的特征注意模块，该模块以粗到精细的方式提取局部遮挡表示和全局图像补丁表示。第二个模块是横图和图形桥模块，该模块融合了图像上下文和手顶点。第三个模块是一种轻巧的跨注意机制，该机制使用元素的操作进行线性复杂性的两只手的交叉注意。与最先进的模型相比，最终的模型在交流中达到了可比的2.6m基准。同时，它将拖鞋降低到$ 0.47Gflops $，而最先进的型号的计算在$ 10gflops $ $至20Gflops $之间。
Recent years have witnessed great success for hand reconstruction in real-time applications such as visual reality and augmented reality while interacting with two-hand reconstruction through efficient transformers is left unexplored. In this paper, we propose a method called lightweight attention hand (LWA-HAND) to reconstruct hands in low flops from a single RGB image. To solve the occlusion and interaction problem in efficient attention architectures, we propose three mobile attention modules in this paper. The first module is a lightweight feature attention module that extracts both local occlusion representation and global image patch representation in a coarse-to-fine manner. The second module is a cross image and graph bridge module which fuses image context and hand vertex. The third module is a lightweight cross-attention mechanism that uses element-wise operation for the cross-attention of two hands in linear complexity. The resulting model achieves comparable performance on the InterHand2.6M benchmark in comparison with the state-of-the-art models. Simultaneously, it reduces the flops to $0.47GFlops$ while the state-of-the-art models have heavy computations between $10GFlops$ and $20GFlops$.
