LCollision: Fast Generation of Collision-Free Human Poses using Learned Non-Penetration Constraints

TL;DR

LCollision is a novel learning-based method that rapidly generates collision-free 3D human poses by predicting collisions and optimizing pose configurations, significantly speeding up collision detection and resolution.

Contribution

It introduces a bilevel autoencoder architecture that decomposes collisions into localized groups and employs constrained optimization for fast, collision-free human pose synthesis.

Findings

Achieves 94.1% collision prediction accuracy

Provides 80x speedup over exact collision detection

First neural network-based approach for collision detection and resolution

Abstract

We present LCollision, a learning-based method that synthesizes collision-free 3D human poses. At the crux of our approach is a novel deep architecture that simultaneously decodes new human poses from the latent space and predicts colliding body parts. These two components of our architecture are used as the objective function and surrogate hard constraints in a constrained optimization for collision-free human pose generation. A novel aspect of our approach is the use of a bilevel autoencoder that decomposes whole-body collisions into groups of collisions between localized body parts. By solving the constrained optimizations, we show that a significant amount of collision artifacts can be resolved. Furthermore, in a large test set of $2.5\times 10^6$ randomized poses from SCAPE, our architecture achieves a collision-prediction accuracy of $94.1\%$ with $80\times$ speedup over exact collision detection algorithms. To the best of our knowledge, LCollision is the first approach that accelerates collision detection and resolves penetrations using a neural network.