论文标题
从单个低分辨率图像中的超分辨率3D人类形状
Super-resolution 3D Human Shape from a Single Low-Resolution Image
论文作者
论文摘要
我们提出了一个新型框架，以从单个低分辨率输入图像中重建超分辨率的人形。该方法克服了从单个图像重建3D人类形状的现有方法的局限性，该方法需要高分辨率图像以及辅助数据，例如表面正常模型或参数模型，以重建高尾形形状。所提出的框架代表具有高确定隐式函数的重建形状。该方法类似于2D图像超分辨率的目的，它从低分辨率形状到其高分辨率对应物的映射学习，并应用于从低分辨率图像中重建3D形状细节。该方法是通过新颖的损失函数进行训练的端到端训练，该功能估计了相同3D表面形状的低分辨率和高分辨率表示之间丢失的信息。对衣服人员进行单图像重建的评估表明，我们的方法从没有辅助数据的低分辨率图像中实现了高确定的表面重建。广泛的实验表明，所提出的方法可以估计超分辨率的人几何形状，其细节水平明显高于将其应用于低分辨率图像时使用的方法。
We propose a novel framework to reconstruct super-resolution human shape from a single low-resolution input image. The approach overcomes limitations of existing approaches that reconstruct 3D human shape from a single image, which require high-resolution images together with auxiliary data such as surface normal or a parametric model to reconstruct high-detail shape. The proposed framework represents the reconstructed shape with a high-detail implicit function. Analogous to the objective of 2D image super-resolution, the approach learns the mapping from a low-resolution shape to its high-resolution counterpart and it is applied to reconstruct 3D shape detail from low-resolution images. The approach is trained end-to-end employing a novel loss function which estimates the information lost between a low and high-resolution representation of the same 3D surface shape. Evaluation for single image reconstruction of clothed people demonstrates that our method achieves high-detail surface reconstruction from low-resolution images without auxiliary data. Extensive experiments show that the proposed approach can estimate super-resolution human geometries with a significantly higher level of detail than that obtained with previous approaches when applied to low-resolution images.
