3D是英文“Three Dimensions”的简称,中文是指三维、三个维度、三个坐标,即有长、有宽、有高,换句话说,就是立体的,是相对于只有长和宽的平面(2D)而言。

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题目: PolyGen: An Autoregressive Generative Model of 3D Meshes

摘要:

多边形网格是三维几何的一种有效表现形式,在计算机图形学、机器人技术和游戏开发中具有重要意义。现有的基于学习的方法避免了使用3D网格的挑战,而是使用与神经结构和训练方法更兼容的替代对象表示。提出了一种直接对网格建模的方法,利用基于变换的结构对网格顶点和面进行顺序预测。我们的模型可以对一系列输入进行条件设置,包括类对象、体素和图像,因为模型是概率性的,所以它可以生成在模糊场景中捕获不确定性的样本。我们证明了该模型能够产生高质量、可用的网格,并为网格建模任务建立了对数似然基准。我们还根据不同的方法评估了表面重建的条件模型,并在没有直接训练的情况下展示了竞争性的表现。

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Existing 3D human pose estimation algorithms trained on distortion-free datasets suffer performance drop when applied to new scenarios with a specific camera distortion. In this paper, we propose a simple yet effective model for 3D human pose estimation in video that can quickly adapt to any distortion environment by utilizing MAML, a representative optimization-based meta-learning algorithm. We consider a sequence of 2D keypoints in a particular distortion as a single task of MAML. However, due to the absence of a large-scale dataset in a distorted environment, we propose an efficient method to generate synthetic distorted data from undistorted 2D keypoints. For the evaluation, we assume two practical testing situations depending on whether a motion capture sensor is available or not. In particular, we propose Inference Stage Optimization using bone-length symmetry and consistency. Extensive evaluation shows that our proposed method successfully adapts to various degrees of distortion in the testing phase and outperforms the existing state-of-the-art approaches. The proposed method is useful in practice because it does not require camera calibration and additional computations in a testing set-up.

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