Bin Wang (王 滨)

Bin Wang (王 滨)

Researcher of Advanced Innovation Center for Future Visual Entertainment

Beijing Film Academy


Bin Wang is currently a full-time researcher of Advanced Innovation Center for Future Visual Entertainment in Beijing Film Academy. She earned her Ph.D in November 2012 from Beihang University, advised by Prof. Shuling Dai. From Aug. 2010 to Aug. 2012, she experienced two-year’s wonderful life in the Sensorimotor System Lab at University of British Columbia, Canada, working with Prof. Dinesh K. Pai and Prof. François Faure, from University Joseph Fourier, Grenoble, France. She has been a Research Fellow from April 2013 to April 2014 in National University of Singapore, supervised by Prof. Kangkang Yin. From 2014 to 2016, she did her postdoctoral researcher at Visual Computing Research Center, leading by Prof. Hui Huang.


  • Computer Graphics
  • Physically Based Modeling and Simulation
  • Material Inverse Modelling.


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Learning Elastic Constitutive Material and Damping Models

This paper presents a new method for estimating nonlinear constitutive models from trajectories of surface data. The key insight is to have a parametric material correction model learn the error of the elastic and damping properties of a nominal material.

A Level-Set Method for Magnetic Substance Simulation

This paper presents a versatile, numerical approach to simulating various magnetic phenomena using a level-set method, which contains a novel two-way coupling mechanism between a magnetic field and magnetizable objects.

Example-driven Virtual Cinematography by Learning Camera Behaviors

In this paper, we propose an example-driven camera controller which can extract camera behaviors from an example film clip and re-apply the extracted behaviors to a 3D animation, through learning from a collection of camera motions.

Deformation Capture and Modeling of Soft Objects

We present a data-driven method that can capture deformation of generic soft objects in high fidelity with low-cost depth sensors; and estimate plausible deformation parameters from these pure kinematic motion trajectories, without requiring any force-displacement measurements as is common in traditional methods. Using the learned deformation models, new motion and deformation can be synthesized at interactive rates to respond to dynamic perturbations or satisfy user-specified constraints.

Simulation and Control of Skeleton-driven Soft Body Characters

We have presented a two-way simulation and control framework for soft characters with inherent skeletons. We propose a novel pose-based plasticity model that extends the corotated linear elasticity model to achieve large skin deformation around joints. We further reconstruct controls from reference trajectories captured from human subjects by augmenting a sampling-based algorithm.