We present a learning-based method (LCollision) 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 classifies the collision status. These two components of our architecture are used as the objective function and surrogate hard-constraints in a constrained-optimization algorithm 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. We show that solving our constrained optimization formulation can resolve significantly more collision artifacts than prior learning algorithms. Furthermore, in a large test set of $2.5\times 10^6$ randomized poses from three major datasets, 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 can obtain high accuracy in terms of handling non-penetration and collision constraints in a learning framework.

中文翻译：

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LCollision：使用学习的非穿透约束快速生成无碰撞人体姿势

我们提出了一种基于学习的方法 (LCollision)，可以合成无碰撞的 3D 人体姿势。我们方法的关键是一种新颖的深度架构，它同时从潜在空间解码新的人体姿势并对碰撞状态进行分类。我们架构的这两个组件被用作目标函数，并在无碰撞人体姿势生成的约束优化算法中替代硬约束。我们方法的一个新颖方面是使用双层自动编码器，将全身碰撞分解为局部身体部位之间的碰撞组。我们表明，与先前的学习算法相比，解决我们的约束优化公式可以解决更多的碰撞伪影。此外，在来自三个主要数据集的 $2.5\times 10^6$ 随机姿势的大型测试集中，我们的架构实现了 $94.1\%$ 的碰撞预测精度，比精确的碰撞检测算法提速了 $80\times$。据我们所知，LCollision 是第一种在学习框架中处理非渗透和碰撞约束方面可以获得高精度的方法。