Total Scale: Face-to-Body Detail Reconstruction from Sparse RGBD Sensors

• URL: http://arxiv.org/abs/2112.02082v1
• Date: Fri, 3 Dec 2021 18:46:49 GMT
• Title: Total Scale: Face-to-Body Detail Reconstruction from Sparse RGBD Sensors
• Authors: Zheng Dong, Ke Xu, Ziheng Duan, Hujun Bao, Weiwei Xu, Rynson W.H. Lau
• Abstract summary: Flat facial surfaces frequently occur in the PIFu-based reconstruction results. We propose a two-scale PIFu representation to enhance the quality of the reconstructed facial details. Experiments demonstrate the effectiveness of our approach in vivid facial details and deforming body shapes.
• Score: 52.38220261632204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While the 3D human reconstruction methods using Pixel-aligned implicit function (PIFu) develop fast, we observe that the quality of reconstructed details is still not satisfactory. Flat facial surfaces frequently occur in the PIFu-based reconstruction results. To this end, we propose a two-scale PIFu representation to enhance the quality of the reconstructed facial details. Specifically, we utilize two MLPs to separately represent the PIFus for the face and human body. An MLP dedicated to the reconstruction of 3D faces can increase the network capacity and reduce the difficulty of the reconstruction of facial details as in the previous one-scale PIFu representation. To remedy the topology error, we leverage 3 RGBD sensors to capture multiview RGBD data as the input to the network, a sparse, lightweight capture setting. Since the depth noise severely influences the reconstruction results, we design a depth refinement module to reduce the noise of the raw depths under the guidance of the input RGB images. We also propose an adaptive fusion scheme to fuse the predicted occupancy field of the body and face to eliminate the discontinuity artifact at their boundaries. Experiments demonstrate the effectiveness of our approach in reconstructing vivid facial details and deforming body shapes, and verify its superiority over state-of-the-art methods.

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