论文标题
COAP：人的组成铰接式占用率
COAP: Compositional Articulated Occupancy of People
论文作者
论文摘要
我们为清晰的人体提供了一种新颖的神经隐式表示。与显式模板网格相比，神经隐式体形表示与环境相互作用提供了有效的机制，这对于3D场景中的人类运动重建和合成至关重要。但是，现有的神经隐式机构对高度表达的姿势的概括或推理时间缓慢而遭受不良的概括。在这项工作中，我们观察到，可以利用有关人体形状和运动学结构的先验知识来提高概括和效率。我们将全身几何形状分解为当地身体部位，并采用部分意识的编码器架构来学习对当地复杂变形进行建模的神经表达占用率。我们的局部形状编码器不仅代表了相应身体部位的身体变形，还代表着相邻的身体部位的变形。解码器结合了局部体形的几何约束，从而显着改善了姿势概括。我们证明我们的模型适合于解决与3D环境的自身交流和碰撞。定量和定性实验表明，在效率和准确性方面，我们的方法在很大程度上优于现有的解决方案。代码和型号可从https://neuralbodies.github.io/coap/index.html获得
We present a novel neural implicit representation for articulated human bodies. Compared to explicit template meshes, neural implicit body representations provide an efficient mechanism for modeling interactions with the environment, which is essential for human motion reconstruction and synthesis in 3D scenes. However, existing neural implicit bodies suffer from either poor generalization on highly articulated poses or slow inference time. In this work, we observe that prior knowledge about the human body's shape and kinematic structure can be leveraged to improve generalization and efficiency. We decompose the full-body geometry into local body parts and employ a part-aware encoder-decoder architecture to learn neural articulated occupancy that models complex deformations locally. Our local shape encoder represents the body deformation of not only the corresponding body part but also the neighboring body parts. The decoder incorporates the geometric constraints of local body shape which significantly improves pose generalization. We demonstrate that our model is suitable for resolving self-intersections and collisions with 3D environments. Quantitative and qualitative experiments show that our method largely outperforms existing solutions in terms of both efficiency and accuracy. The code and models are available at https://neuralbodies.github.io/COAP/index.html
