Garment animation is ubiquitous in various applications, such as virtual reality, gaming, and film producing. Recently, learning-based approaches obtain compelling performance in animating diverse garments under versatile scenarios. Nevertheless, to mimic the deformations of the observed garments, data-driven methods require large scale of garment data, which are both resource-wise expensive and time-consuming. In addition, forcing models to match the dynamics of observed garment animation may hinder the potentials to generalize to unseen cases. In this paper, instead of using garment-wise supervised-learning we adopt a disentangled scheme to learn how to animate observed garments: 1). learning constitutive behaviors from the observed cloth; 2). dynamically animate various garments constrained by the learned constitutive laws. Specifically, we propose Energy Unit network (EUNet) to model the constitutive relations in the format of energy. Without the priors from analytical physics models and differentiable simulation engines, EUNet is able to directly capture the constitutive behaviors from the observed piece of cloth and uniformly describes the change of energy caused by deformations, such as stretching and bending. We further apply the pre-trained EUNet to animate various garments based on energy optimizations. The disentangled scheme alleviates the need of garment data and enables us to utilize the dynamics of a piece of cloth for animating garments. Experiments show that while EUNet effectively delivers the energy gradients due to the deformations, models constrained by EUNet achieve more stable and physically plausible performance comparing with those trained in garment-wise supervised manner.

Mimicking realistic dynamics in 3D garment animations is a challenging task due to the complex nature of multi-layered garments and the variety of outer forces involved. Existing approaches mostly focus on single-layered garments driven by only human bodies and struggle to handle general scenarios. In this paper, we propose a novel data-driven method, called LayersNet, to model garment-level animations as particle-wise interactions in a micro physics system. We improve simulation efficiency by representing garments as patch-level particles in a two-level structural hierarchy. Moreover, we introduce a novel Rotation Equivalent Transformation with Rotation Invariant Attention that leverage the rotation invariance and additivity of physics systems to better model outer forces. To verify the effectiveness of our approach and bridge the gap between experimental environments and real-world scenarios, we introduce a new challenging dataset, D-LAYERS, containing 700K frames of dynamics of 4,900 combinations of multi-layered garments driven by human bodies and randomly sampled wind. Our LayersNet achieves superior performance both quantitatively and qualitatively. Project page: www.mmlab-ntu.com/project/layersnet/index.html.

In this paper, the issue of user-centric clustering in cloud radio access networks is investigated. Specially, the outage probability of the typical user is derived by taking void cell and composite fading into account. The locations of remote radio heads and users working on the same resource block are modeled as the Poisson point process (PPP) and Mat$\acute {e}$rn hard-core point process of type II (MHCPP), respectively. Due to intractability of MHCPP, the PPP-based approximation is adopted. Then the closed expression of Laplace transform of the probability density function of the interfering power under composite fading channels is derived by Gauss-Hermite quadrature. Based on the approximated PPP, we obtain the outage probability of the typical user with user-centric clustering in the presence of void cell. Finally, the outage probability with or without considering void cell is verified and analyzed under different system parameters (i.g., the density of nodes, the radius of cluster, and the threshold of signal quality) and channel fading conditions (i.g., the pathloss exponent, shadowing standard deviation) through extensive simulations. Simulation results show that the effect of void cell on the system performance should be considered, especially when the density of nodes or the size of cluster is limited.

Along with the dramatic rising of data rate, user-centric network (UCN) is regarded as a promising concept. However, densely deployed same-frequency small base stations (SBSs) in UCN deteriorates the user performance. Coordinated multipoint (CoMP) is an important technique for alleviating inter-cell interference. In this paper, to combat the severe interference, we proposed a user-centric cooperation scheme-Voronoi CoMP in a cell-less architecture. In this scheme, users are assigned their own clusters of SBSs respectively and the clusters for different users are not overlapped. This CoMP structure ensures that each user served by the base stations which are the closest ones of all SBSs to it and this will maximize the useful power of each user and in the meantime minimize the interference. The numerical results and simulations show that the proposed CoMP scheme can indeed promote the performance of dense small cell network (DSCN) corresponding to the designed metrics.