To adequately utilize the available image evidence in multi-view video-based
avatar modeling, we propose TexVocab, a novel avatar representation that
constructs a texture vocabulary and associates body poses with texture maps for
animation. Given multi-view RGB videos, our method initially back-projects all
the available images in the training videos to the posed SMPL surface,
producing texture maps in the SMPL UV domain. Then we construct pairs of human
poses and texture maps to establish a texture vocabulary for encoding dynamic
human appearances under various poses. Unlike the commonly used joint-wise
manner, we further design a body-part-wise encoding strategy to learn the
structural effects of the kinematic chain. Given a driving pose, we query the
pose feature hierarchically by decomposing the pose vector into several body
parts and interpolating the texture features for synthesizing fine-grained
human dynamics. Overall, our method is able to create animatable human avatars
with detailed and dynamic appearances from RGB videos, and the experiments show
that our method outperforms state-of-the-art approaches. The project page can
be found at this https URL

提出了一种名为 TexVocab 的新方法，通过构建纹理词汇表并将人体姿势与纹理映射关联，从而充分利用多视角基于视频的人物建模中可用的图像证据，以实现细致和动态的人物表现。

TexVocab: 基于纹理词汇的人体动画

TexVocab: Texture Vocabulary-conditioned Human Avatars

Understanding human actions from body poses is critical for assistive robots
sharing space with humans in order to make informed and safe decisions about
the next interaction. However, precise temporal localization and annotation of
activity sequences is time-consuming and the resulting labels are often noisy.
If not effectively addressed, label noise negatively affects the model's
training, resulting in lower recognition quality. Despite its importance,
addressing label noise for skeleton-based action recognition has been
overlooked so far. In this study, we bridge this gap by implementing a
framework that augments well-established skeleton-based human action
recognition methods with label-denoising strategies from various research areas
to serve as the initial benchmark. Observations reveal that these baselines
yield only marginal performance when dealing with sparse skeleton data.
Consequently, we introduce a novel methodology, NoiseEraSAR, which integrates
global sample selection, co-teaching, and Cross-Modal Mixture-of-Experts
(CM-MOE) strategies, aimed at mitigating the adverse impacts of label noise.
Our proposed approach demonstrates better performance on the established
benchmark, setting new state-of-the-art standards. The source code for this
study will be made accessible at this https URL

理解人体姿势的行为对于与人类共享空间的辅助机器人来说是至关重要的，为了能够对下一次交互做出明智和安全的决策。然而，准确的时间定位和标注活动序列是耗时的，而且结果往往带有噪声。本研究通过将骨架动作识别方法与来自多个研究领域的标签去噪策略相结合，填补了这一空白，为此实施了一个框架作为初始基准。观察结果显示，这些基准在处理稀疏骨架数据时仅能提供较小的性能。因此，我们提出了一种新的方法，NoiseEraSAR，它集成了全局样本选择、共同教学和交叉模态混合专家（CM-MOE）策略，旨在减轻标签噪声的不利影响。我们的方法在已建立的基准上展示了更好的性能，树立了新的最先进标准。本研究的源代码将在此 https URL 上公开。