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

Qingyang Tan
Qingyang Tan
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Hybrid Ranking, Potential Energy, and Entropy Loss: exact hard constraints correspond to a binary loss, this loss should be differentiable so that constrained optimizations can be guided by gradient information

Abstract:

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 su...More

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Introduction
  • There has been considerable work on developing learning algorithms on 3D objects, represented as point clouds (Qi et al 2017), meshes (Hanocka et al 2019), volumetric grids (Wang, Liu, and Tong 2020), and physical objects (Li et al 2019).
  • These methods learn a manifold of plausible human poses from a dataset, represented as the latent space of a deep autoencoder.
  • After learning the feasible domain, solving a constrained optimization for a collision-free human pose with 2161 vertices takes 2.095 iterations and 0.25s on average.
Highlights
  • There has been considerable work on developing learning algorithms on 3D objects, represented as point clouds (Qi et al 2017), meshes (Hanocka et al 2019), volumetric grids (Wang, Liu, and Tong 2020), and physical objects (Li et al 2019). As these algorithms are used for different applications, a major challenge is accounting for user requirements and physics-based constraints
  • Each point on the human body is softly assigned to a set of local domains, and the collision penalty loss is decomposed to these local domains
  • Hybrid Ranking, Potential Energy, and Entropy Loss: exact hard constraints correspond to a binary loss, this loss should be differentiable so that constrained optimizations can be guided by gradient information
  • We propose using a penetration depth formulation (Zhang et al 2007) as collision metric to offer gradient direction and use ranking loss to enforce the relative differences between each sample
  • We review related works on human pose estimation and synthesis, collision detection and response, and deep network training with hard constraints
Results
  • The authors review related works on human pose estimation and synthesis, collision detection and response, and deep network training with hard constraints.
  • The authors' approach is based on recent learning methods (Tan et al 2018a; Tretschk et al 2020) that use 3D meshes to generate detailed human poses.
  • The authors can use different collision handling methods to avoid penetrations in a 3D mesh of a human pose.
  • Training Deep Networks with Hard Constraints: An additional layer of challenge is to incorporate collision handling into a deep learning framework.
  • The authors give an overview of the process of computing the embedding space for human pose generation and highlight the collision-free constraints that LCollision tries to satisfy.
  • The learned domain decomposition enhances the reusability and explainability of the neural network but is used to model the local collisions between body sub-parts, as explained in Section 3.3.
  • This constraint is ignored by previous neural-network-based human pose generation methods.
  • Given a mesh G, the authors use the FCL library (Pan, Chitta, and Manocha 2012) to compute the squared penetration depth PD2p,q of each colliding triangle pair.
  • The authors train a single classifier MLPclassifier(S1, ⋯, S Z0 ) to summarize the information and predict whether there are any collisions throughout the human body, i.e. MLPclassifier is an indicator of whether Ssum = 0.
  • To profile the collision response solver quantitatively, the authors sample a set of 3000 random human poses by randomizing Zall for both the SCAPE and Swing datasets.
  • On the SCAPE dataset, the method achieves a success rate of 85.6%, and the authors observe a relative decrease of 80.9% for these models compared to the original penetration depth energy.
Conclusion
  • The authors present a method for learning the collision-free human pose sub-manifold.
  • The authors use a mesh embedding autoencoder to learn a full human pose manifold and augment it with an additional component to classify the collision and other hard constraints.
  • The authors learn to predict the penetration depths aggregated to each sub-domain and use a binary classifier to predict whether a given mesh has any collisions.
Summary
  • There has been considerable work on developing learning algorithms on 3D objects, represented as point clouds (Qi et al 2017), meshes (Hanocka et al 2019), volumetric grids (Wang, Liu, and Tong 2020), and physical objects (Li et al 2019).
  • These methods learn a manifold of plausible human poses from a dataset, represented as the latent space of a deep autoencoder.
  • After learning the feasible domain, solving a constrained optimization for a collision-free human pose with 2161 vertices takes 2.095 iterations and 0.25s on average.
  • The authors review related works on human pose estimation and synthesis, collision detection and response, and deep network training with hard constraints.
  • The authors' approach is based on recent learning methods (Tan et al 2018a; Tretschk et al 2020) that use 3D meshes to generate detailed human poses.
  • The authors can use different collision handling methods to avoid penetrations in a 3D mesh of a human pose.
  • Training Deep Networks with Hard Constraints: An additional layer of challenge is to incorporate collision handling into a deep learning framework.
  • The authors give an overview of the process of computing the embedding space for human pose generation and highlight the collision-free constraints that LCollision tries to satisfy.
  • The learned domain decomposition enhances the reusability and explainability of the neural network but is used to model the local collisions between body sub-parts, as explained in Section 3.3.
  • This constraint is ignored by previous neural-network-based human pose generation methods.
  • Given a mesh G, the authors use the FCL library (Pan, Chitta, and Manocha 2012) to compute the squared penetration depth PD2p,q of each colliding triangle pair.
  • The authors train a single classifier MLPclassifier(S1, ⋯, S Z0 ) to summarize the information and predict whether there are any collisions throughout the human body, i.e. MLPclassifier is an indicator of whether Ssum = 0.
  • To profile the collision response solver quantitatively, the authors sample a set of 3000 random human poses by randomizing Zall for both the SCAPE and Swing datasets.
  • On the SCAPE dataset, the method achieves a success rate of 85.6%, and the authors observe a relative decrease of 80.9% for these models compared to the original penetration depth energy.
  • The authors present a method for learning the collision-free human pose sub-manifold.
  • The authors use a mesh embedding autoencoder to learn a full human pose manifold and augment it with an additional component to classify the collision and other hard constraints.
  • The authors learn to predict the penetration depths aggregated to each sub-domain and use a binary classifier to predict whether a given mesh has any collisions.
Tables
  • Table1: We compare our method (Ours) with 4 baselines: Lentropy + LP D, Lentropy + Lrank, Lentropy, and ND (no collision decomposition). For each method, we train on the smaller dataset with M = 5 × 104 meshes. For each trained dataset, we compare their accuracy in terms of predicting penetration depth energies (MSE), ranking penetration depth energies (RANK), and classifying collision-free meshes (CLASSIFY). Compared with Lentropy+LP D, Lentropy+Lrank, and Lentropy, we see the power of our hybrid loss to improve the overall accuracy of collision predictions. The improvement from N D to our method demonstrates that the penetration decomposition is meaningful in our framework
  • Table2: We study the robustness of our method in terms of dataset sizes. Increasing the dataset size M can significantly boost the collision detection accuracy (CLASSIFY). This result implies that learning to predict collisions is challenging, and a larger training dataset can help improve the overall results
  • Table3: We show the collision detection running time for our method compared with one exact collision detection algorithm (<a class="ref-link" id="cPan_et+al_2012_a" href="#rPan_et+al_2012_a"><a class="ref-link" id="cPan_et+al_2012_a" href="#rPan_et+al_2012_a">Pan, Chitta, and Manocha 2012</a></a>). All datasets have 1.5 × 104 samples. Swing and Jumping have more points than SCAPE (9971 and 10002 vs 2261), and the complexity of (<a class="ref-link" id="cPan_et+al_2012_a" href="#rPan_et+al_2012_a"><a class="ref-link" id="cPan_et+al_2012_a" href="#rPan_et+al_2012_a">Pan, Chitta, and Manocha 2012</a></a>) depends on the number of points, thus we spent more time on them for exact collision checking. While, they all share the same level of latent space size with SCAPE and the running times for our method are similar
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Related work
  • We review related works on human pose estimation and synthesis, collision detection and response, and deep network training with hard constraints.

    Human Pose Estimation & Synthesis: There is considerable work on human pose estimation and synthesis. Earlier methods (Leibe, Seemann, and Schiele 2005) represent a pedestrian as a bounding box. An improved algorithm was proposed in (Agarwal and Triggs 2005), and this algorithm predicts the 55-D joint angles for a skeletal human pose. More accurate prediction results have been proposed in (Rogez et al 2008) using random forests and in (Toshev and Szegedy 2014) using convolutional neural networks. Our approach is based on recent learning methods (Tan et al 2018a; Tretschk et al 2020) that use 3D meshes to generate detailed human poses. Mesh-based representations are inherently difficult to learn due to the intrinsic high-dimensionality, and the resulting algorithms can produce sub-optimal results that may consist of various artifacts such as self-penetrations, noisy mesh surfaces, and flipped meshes. In view of these problems, (Villegas et al 2018) only computes skeletal poses using learning and then uses skinning to recover the mesh-based representation. However, this approach requires additional skeleton-mesh correspondence information, which is typically unavailable in many datasets including SCAPE (Anguelov et al 2005).
Funding
  • We propose using a penetration depth formulation (Zhang et al 2007) as collision metric to offer gradient direction and then use ranking loss to enforce the relative differences between each sample. We have implemented these algorithms and evaluated the performance on the SCAPE dataset (Anguelov et al 2005), the MIT-Swing dataset (Vlasic et al 2008), and the MIT Jumping dataset (Vlasic et al 2008). Combining these techniques, we achieve an accuracy of 94.1%, a false positive rate of 6.1%, and a false negative rate of 5.7% when predicting collisions for 2.5 × 106 randomized testing poses from these datasets
  • To classify collision-free meshes, we use the rate of success (CLASSIFY) over the 0.3M test meshes. From this ablation study, we compare N D and our method to find that penetration decomposition can improve the accuracy of collision predictions
  • Being a learning-based method, our collision predictor cannot achieve a 100% success rate, in contrast to analytic methods (Barbicand James 2010)
Study subjects and analysis
major datasets: 3
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

datasets: 3
This stage uses the loss: L = wP DLP D + wrankLrank + wentropyLentropy, which is configured with wP D = 5, wrank = 2, and wentropy = 2 and trained using a learning rate of 0.001 and a batch size of 32 over 30 epochs. We evaluate our method on three datasets: the SCAPE dataset (Anguelov et al 2005) with N = 71 meshes, the MIT-Swing dataset (Vlasic et al 2008) with N = 150 meshes, and the MIT Jumping dataset (Vlasic et al 2008) with N = 150 meshes. For each dataset, we use all the meshes to train the embedding space during the first stage

samples: 104
1.02s 0.91s 1.13s. 81x 342x 282x the test set of 5 × 104 samples (1.5 × 104 samples) for the SCAPE, Swing, and Jumping datasets. To achieve the best performance for (Pan, Chitta, and Manocha 2012), we run their method using 15 threads in parallel and stop when one collision occurs or the process is reported collision-free

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