High-Resolution Volumetric Reconstruction for Clothed Humans
Sicong Tang Guangyuan Wang Qing Ran Lingzhi Li Li Shen Ping Tan
We present a novel method for reconstructing clothed humans from a sparse set
of, e.g., 1 to 6 RGB images. Despite impressive results from recent works
employing deep implicit representation, we revisit the volumetric approach and
demonstrate that better performance can be achieved with proper system design.
The volumetric representation offers significant advantages in leveraging 3D
spatial context through 3D convolutions, and the notorious quantization error
is largely negligible with a reasonably large yet affordable volume resolution,
e.g., 512. To handle memory and computation costs, we propose a sophisticated
coarse-to-fine strategy with voxel culling and subspace sparse convolution. Our
method starts with a discretized visual hull to compute a coarse shape and then
focuses on a narrow band nearby the coarse shape for refinement. Once the shape
is reconstructed, we adopt an image-based rendering approach, which computes
the colors of surface points by blending input images with learned weights.
Extensive experimental results show that our method significantly reduces the
mean point-to-surface (P2S) precision of state-of-the-art methods by more than
50% to achieve approximately 2mm accuracy with a 512 volume resolution.
Additionally, images rendered from our textured model achieve a higher peak
signal-to-noise ratio (PSNR) compared to state-of-the-art methods.