  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.
