IEEE-Transactions-on-Visualization-and-Computer-Graphics_TVCG.md

TVCG (IEEE Transactions on Visualization and Computer Graphics)

• Wang, Z., Chai, J. and Xia, S., 2019. Combining recurrent neural networks and adversarial training for human motion synthesis and control. IEEE Transactions on Visualization and Computer Graphics, 27(1), pp.14-28. [ www ] ( PSO | Continuous Optimization )
• Agrawal, S., Shen, S. and van de Panne, M., 2014. Diverse motions and character shapes for simulated skills. IEEE Transactions on Visualization and Computer Graphics, 20(10), pp.1345-1355. [ www ] ( CMA-ES | Continuous Optimization )
  • "Our primary contribution is a diversity optimization framework that allows for the synthesis of sets of simulated motions and character shapes that span the many possible ways in which an underconstrained motion can be achieved. As key elements of this framework, we propose: (1) an objective function tailored to producing diverse motions; (2) the exploration of shape diversity in order to produce motion variations that result from adaptation to a wide variety of character shapes; (3) the use of round-robin covariance matrix adaptation (CMA) as an effective optimization strategy; and (4) the use of known pose similarity metrics as being equally suitable as distance metrics for measuring motion diversity and character shape diversity."
    • K. Sims, “Evolving virtual creatures,” in Proc. 21st Annu. Conf. Comput. Graph. Inter. Tech., New York, NY, USA, 1994, pp. 15–22.
    • N. Hansen, “The CMA evolution strategy: A comparing review,” in Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms, J. Lozano, P. Larranaga, I. Inza, and E. Bengoetxea, eds., New York, NY, USA: Springer, 2006, 75–102.