Clothing is an important part of human appearance but challenging to model in
photorealistic avatars. In this work we present avatars with dynamically moving
loose clothing that can be faithfully driven by sparse RGB-D inputs as well as
body and face motion. We propose a Neural Iterative Closest Point (N-ICP)
algorithm that can efficiently track the coarse garment shape given sparse
depth input. Given the coarse tracking results, the input RGB-D images are then
remapped to texel-aligned features, which are fed into the drivable avatar
models to faithfully reconstruct appearance details. We evaluate our method
against recent image-driven synthesis baselines, and conduct a comprehensive
analysis of the N-ICP algorithm. We demonstrate that our method can generalize
to a novel testing environment, while preserving the ability to produce
high-fidelity and faithful clothing dynamics and appearance.

本研究介绍了一种神经迭代最近点（N-ICP）算法，通过稀疏的 RGB-D 输入和身体及面部运动，能够准确地驱动动态移动的宽松服装，从而创建具有逼真外观的虚拟角色模型。我们通过与最近的图像驱动合成基准的比较和对 N-ICP 算法的全面分析来评估我们的方法，并展示了我们的方法在新的测试环境中的泛化能力，同时保持了产生高保真度和真实服装动态外观的能力。

可驾驶的阿凡达服装：由稀疏的 RGB-D 输入驱动的忠实全身远程存在感与动态服装

Drivable Avatar Clothing: Faithful Full-Body Telepresence with Dynamic  Clothing Driven by Sparse RGB-D Input

This work addresses multi-class segmentation of indoor scenes with RGB-D
inputs. While this area of research has gained much attention recently, most
works still rely on hand-crafted features. In contrast, we apply a multiscale
convolutional network to learn features directly from the images and the depth
information. We obtain state-of-the-art on the NYU-v2 depth dataset with an
accuracy of 64.5%. We illustrate the labeling of indoor scenes in videos
sequences that could be processed in real-time using appropriate hardware such
as an FPGA.

采用多尺度卷积神经网络直接从图像和深度信息中学习特征实现室内场景的多类别分割，并在 NYU-v2 深度数据集上取得了 64.5% 的准确率，充分说明了适当的硬件（如 FPGA）可以实现实时处理。