TexVocab：纹理词汇条件化的人体化身
TexVocab: Texture Vocabulary-conditioned Human Avatars
AI总结：
TexVocab通过构建纹理词汇并将姿态与纹理图关联，结合逐身体部位编码策略，从多视角RGB视频中生成细节丰富且动态的可动画人体化身，性能优于现有方法。
摘要：
为了在多视角视频驱动的化身建模中充分利用可用的图像证据，我们提出了TexVocab，一种新颖的化身表示方法，该方法构建纹理词汇并将身体姿态与纹理图关联起来以用于动画生成。给定多视角RGB视频，我们的方法首先将训练视频中所有可用的图像反向投影到姿态化的SMPL表面上，在SMPL UV域中生成纹理图。然后，我们构建人体姿态与纹理图的配对，以建立一个纹理词汇，用于编码各种姿态下的动态人体外观。与常用的逐关节方式不同，我们进一步设计了一种逐身体部位编码策略，以学习运动链的结构效应。给定一个驱动姿态，我们通过将姿态向量分解为若干身体部位并插值纹理特征来分层查询姿态特征，从而合成细粒度的人体动态。总体而言，我们的方法能够从RGB视频中创建具有细节丰富且动态外观的可动画人体化身，实验表明我们的方法优于最先进的方法。项目页面可在https://texvocab.github.io/找到。
英文摘要：
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 https://texvocab.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
