We present three novel strategies to incorporate a parametric body model into a pixel-aligned implicit model for single-view clothed human reconstruction. Firstly, we introduce ray-based sampling, a novel technique that transforms a parametric model into a set of highly informative, pixel-aligned 2D feature maps. Next, we propose a new type of feature based on blendweights. Blendweight-based labels serve as soft human parsing labels and help to improve the structural fidelity of reconstructed meshes. Finally, we show how we can extract and capitalize on body part orientation information from a parametric model to further improve reconstruction quality. Together, these three techniques form our S-PIFu framework, which significantly outperforms state-of-the-arts methods in all metrics. Our code is available at https://github.com/kcyt/SPIFu.

In Figure 1, we show S-PIFu's results when given images of test subjects who wear large clothings (e.g. SMPL-X body, and yet S-PIFu is able reconstruct the human subjects accurately. Pixels that belong to human subject but not to the SMPL-X body act as a natural regularizer that prevents S-PIFu from being overly reliant on estimated SMPL-X meshes to reconstruct clothed human meshes. This happens because these pixels only have valid values for the RGB channels and not the channels of our 2D feature maps (i.e. C, B, and N. Recall that C refers to coordinate In Figure 1, we observe what would happen if we feed a noisy SMPL-X mesh (i.e. a SMPL-X mesh SMPL-X mesh's arms (both arms).

We present three novel strategies to incorporate a parametric body model into a pixel-aligned implicit model for single-view clothed human reconstruction. Firstly, we introduce ray-based sampling, a novel technique that transforms a parametric model into a set of highly informative, pixel-aligned 2D feature maps. Next, we propose a new type of feature based on blendweights. Blendweight-based labels serve as soft human parsing labels and help to improve the structural fidelity of reconstructed meshes. Finally, we show how we can extract and capitalize on body part orientation information from a parametric model to further improve reconstruction quality.