论文标题
逼真的单眼3D重建人穿着衣服的人
Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing
论文作者
论文摘要
我们提出了Phorhum，这是一种新颖的端到端可训练的，深层的神经网络方法，用于仅鉴于单眼RGB图像，用于3D人类重建。我们的像素一致的方法估算了详细的3D几何形状，并首次将未阴影的表面颜色与场景照明一起估算。观察到仅3D监督不足以进行高保真颜色重建，我们引入了基于贴片的渲染损失，从而在人类可见部分上可靠地重建可靠的颜色重建，并对不可易见的部分进行详细且可见的颜色估计。此外，在代表几何，反照率和照明效应方面，我们的方法专门解决了先前工作的方法论和实际局限性，在端到端模型中，可以有效地分解因素。在广泛的实验中，我们证明了方法的多功能性和鲁棒性。我们的最先进的结果是定性和针对不同指标的几何和颜色重建的验证方法。
We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for photorealistic 3D human reconstruction given just a monocular RGB image. Our pixel-aligned method estimates detailed 3D geometry and, for the first time, the unshaded surface color together with the scene illumination. Observing that 3D supervision alone is not sufficient for high fidelity color reconstruction, we introduce patch-based rendering losses that enable reliable color reconstruction on visible parts of the human, and detailed and plausible color estimation for the non-visible parts. Moreover, our method specifically addresses methodological and practical limitations of prior work in terms of representing geometry, albedo, and illumination effects, in an end-to-end model where factors can be effectively disentangled. In extensive experiments, we demonstrate the versatility and robustness of our approach. Our state-of-the-art results validate the method qualitatively and for different metrics, for both geometric and color reconstruction.
