Shallow Signed Distance Functions for Kinematic Collision Bodies

TL;DR

This paper introduces a collection of shallow neural networks to efficiently represent signed distance functions for kinematically deforming human avatars, enabling real-time collision detection in clothing simulation.

Contribution

It proposes a novel approach using multiple shallow SDF networks with a stitching process for fast, accurate, real-time avatar collision queries during clothing simulation.

Findings

Achieves real-time collision detection with high accuracy.

Demonstrates applicability in real-time garment simulation.

Outperforms traditional DeepSDF methods in speed and efficiency.

Abstract

We present learning-based implicit shape representations designed for real-time avatar collision queries arising in the simulation of clothing. Signed distance functions (SDFs) have been used for such queries for many years due to their computational efficiency. Recently deep neural networks have been used for implicit shape representations (DeepSDFs) due to their ability to represent multiple shapes with modest memory requirements compared to traditional representations over dense grids. However, the computational expense of DeepSDFs prevents their use in real-time clothing simulation applications. We design a learning-based representation of SDFs for human avatars whoes bodies change shape kinematically due to joint-based skinning. Rather than using a single DeepSDF for the entire avatar, we use a collection of extremely computationally efficient (shallow) neural networks that represent localized deformations arising from changes in body shape induced by the variation of a single joint. This requires a stitching process to combine each shallow SDF in the collection together into one SDF representing the signed closest distance to the boundary of the entire body. To achieve this we augment each shallow SDF with an additional output that resolves whether or not the individual shallow SDF value is referring to a closest point on the boundary of the body, or to a point on the interior of the body (but on the boundary of the individual shallow SDF). Our model is extremely fast and accurate and we demonstrate its applicability with real-time simulation of garments driven by animated characters.