We propose SMPLitex, a method for estimating and manipulating the complete 3D
appearance of humans captured from a single image. SMPLitex builds upon the
recently proposed generative models for 2D images, and extends their use to the
3D domain through pixel-to-surface correspondences computed on the input image.
To this end, we first train a generative model for complete 3D human
appearance, and then fit it into the input image by conditioning the generative
model to the visible parts of the subject. Furthermore, we propose a new
dataset of high-quality human textures built by sampling SMPLitex conditioned
on subject descriptions and images. We quantitatively and qualitatively
evaluate our method in 3 publicly available datasets, demonstrating that
SMPLitex significantly outperforms existing methods for human texture
estimation while allowing for a wider variety of tasks such as editing,
synthesis, and manipulation

我们提出了 SMPLitex，这是一种从单张图片中估计和操作人类完整 3D 外观的方法。SMPLitex 基于最近提出的 2D 图像生成模型，并通过对输入图像上的像素到表面对应进行计算将其扩展到 3D 领域。我们首先训练了一个完整的 3D 人类外观生成模型，然后通过将生成模型与主体的可见部分相关联，将其适应于输入图像。此外，我们提出了一个新的高质量人类纹理数据集，通过对 SMPLitex 进行主体描述和图像采样构建。我们在三个公开数据集中进行定量和定性评估，结果显示 SMPLitex 明显优于现有的人类纹理估计方法，同时允许进行更多样化的任务，如编辑，合成和操作。

SMPLitex：基于单个图像的三维人体纹理估计的生成模型和数据集

SMPLitex: A Generative Model and Dataset for 3D Human Texture Estimation  from Single Image

Humans excel at grasping objects and manipulating them. Capturing human
grasps is important for understanding grasping behavior and reconstructing it
realistically in Virtual Reality (VR). However, grasp capture - capturing the
pose of a hand grasping an object, and orienting it w.r.t. the object - is
difficult because of the complexity and diversity of the human hand, and
occlusion. Reflective markers and magnetic trackers traditionally used to
mitigate this difficulty introduce undesirable artifacts in images and can
interfere with natural grasping behavior. We present preliminary work on a
completely marker-less algorithm for grasp capture from a video depicting a
grasp. We show how recent advances in 2D hand pose estimation can be used with
well-established optimization techniques. Uniquely, our algorithm can also
capture hand-object contact in detail and integrate it in the grasp capture
process. This is work in progress, find more details at this https URL
cc.gatech.edu/grasp_capture.html.

研究了通过使用最新的二维手姿势估计技术和优化技术进行的无标记算法来捕捉手抓握物体的过程，此算法也可以详细地捕捉手 - 物体接触并将其集成进抓握捕捉过程中。