Sphere Face Model: A 3D morphable model with hypersphere manifold latent space using joint 2D/3D training

3D morphable models(3DMMs)are generative models for face shape and appearance.Recent works impose face recognition constraints on 3DMM shape parameters so that the face shapes of the same person remain consistent.However,the shape parameters of traditional 3DMMs satisfy the multivariate Gaussian distribution.In contrast,the identity embeddings meet the hypersphere distribution,and this conflict makes it challenging for face reconstruction models to preserve the faithfulness and the shape consistency simultaneously.In other words,recognition loss and reconstruction loss can not decrease jointly due to their conflict distribution.To address this issue,we propose the Sphere Face Model(SFM),a novel 3DMM for monocular face reconstruction,preserving both shape fidelity and identity consistency.The core of our SFM is the basis matrix which can be used to reconstruct 3D face shapes,and the basic matrix is learned by adopting a twostage training approach where 3D and 2D training data are used in the first and second stages,respectively.We design a novel loss to resolve the distribution mismatch,enforcing that the shape parameters have the hyperspherical distribution.Our model accepts 2D and 3D data for constructing the sphere face models.Extensive experiments show that SFM has high representation ability and clustering performance in its shape parameter space.Moreover,it produces highfidelity face shapes consistently in challenging conditions in monocular face reconstruction.The code will be released at https://github.com/a686432/SIR.

A three-stage real-time detector for traffic signs in large panoramas

Traffic sign detection is one of the key components in autonomous driving.Advanced autonomous vehicles armed with high quality sensors capture high definition images for further analysis.Detecting traffic signs,moving vehicles,and lanes is important for localization and decision making.Traffic signs,especially those that are far from the camera,are small,and so are challenging to traditional object detection methods.In this work,in order to reduce computational cost and improve detection performance,we split the large input images into small blocks and then recognize traffic signs in the blocks using another detection module.Therefore,this paper proposes a three-stage traffic sign detector,which connects a Block Net with an RPN–RCNN detection network.Block Net,which is composed of a set of CNN layers,is capable of performing block-level foreground detection,making inferences in less than 1 ms.Then,the RPN–RCNN two-stage detector is used to identify traffic sign objects in each block;it is trained on a derived dataset named TT100 KPatch.Experiments show that our framework can achieve both state-of-the-art accuracy and recall;its fastest detection speed is 102 fps.

Efficient and robust strain limiting and treatment of simultaneous collisions with semidefinite programming

We present an efficient and robust method which performs well for both strain limiting and treatment of simultaneous collisions. Our method formulates strain constraints and collision constraints as a serial of linear matrix inequalities(LMIs)and linear polynomial inequalities(LPIs), and solves an optimization problem with standard convex semidefinite programming solvers. When performing strain limiting, our method acts on strain tensors to constrain the singular values of the deformation gradient matrix in a specified interval. Our method can be applied to both triangular surface meshes and tetrahedral volume meshes. Compared with prior strain limiting methods, our method converges much faster and guarantees triangle flipping does not occur when applied to a triangular mesh. When performing treatment of simultaneous collisions, our method eliminates all detected collisions during each iteration,leading to higher efficiency and faster convergence than prior collision treatment methods.

3D corrective nose reconstruction from a single image

There is a steadily growing range of applications that can benefit from facial reconstruction techniques,leading to an increasing demand for reconstruction of high-quality 3D face models.While it is an important expressive part of the human face,the nose has received less attention than other expressive regions in the face reconstruction literature.When applying existing reconstruction methods to facial images,the reconstructed nose models are often inconsistent with the desired shape and expression.In this paper,we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image,where 3D and 2D nose curve correspondences are adaptively updated and refined.We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences,and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result.A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints.Experimental results show the advantages of our method for 3D nose reconstruction over existing methods.

GPU based real-time simulation of massive falling leaves

As an important autumn feature,scenes with large numbers of falling leaves are common in movies and games. However,it is a challenge for computer graphics to simulate such scenes in an authentic and efficient manner. This paper proposes a GPU based approach for simulating the falling motion of many leaves in real time. Firstly,we use a motionsynthesis based method to analyze the falling motion of the leaves,which enables us to describe complex falling trajectories using low-dimensional features. Secondly,we transmit a primitive-motion trajectory dataset together with the low-dimensional features of the falling leaves to video memory,allowing us to execute the appropriate calculations on the GPU.