Point-Based Modeling of Human Clothing
This addresses the need for flexible and efficient clothing modeling in computer graphics and virtual reality applications, representing a novel method rather than an incremental improvement.
The paper tackles the problem of modeling human clothing by proposing a point cloud-based approach that can predict outfits for various poses and body shapes from a single image, and also captures appearance from videos using neural point-based graphics.
We propose a new approach to human clothing modeling based on point clouds. Within this approach, we learn a deep model that can predict point clouds of various outfits, for various human poses, and for various human body shapes. Notably, outfits of various types and topologies can be handled by the same model. Using the learned model, we can infer the geometry of new outfits from as little as a single image, and perform outfit retargeting to new bodies in new poses. We complement our geometric model with appearance modeling that uses the point cloud geometry as a geometric scaffolding and employs neural point-based graphics to capture outfit appearance from videos and to re-render the captured outfits. We validate both geometric modeling and appearance modeling aspects of the proposed approach against recently proposed methods and establish the viability of point-based clothing modeling.