In a conventional optical motion capture (MoCap) workflow, two processes are needed to turn captured raw marker sequences into correct skeletal animation sequences. Firstly, various tracking errors present in the markers must be fixed ( cleaning or refining ). Secondly, an agent skeletal mesh must be prepared for the actor/actress, and used to determine skeleton information from the markers ( re-targeting or solving ). The whole process, normally referred to as solving MoCap data, is extremely time-consuming, labor-intensive, and usually the most costly part of animation production. Hence, there is a great demand for automated tools in industry. In this work, we present MoCap-Solver, a production-ready neural solver for optical MoCap data. It can directly produce skeleton sequences and clean marker sequences from raw MoCap markers, without any tedious manual operations. To achieve this goal, our key idea is to make use of neural encoders concerning three key intrinsic components: the template skeleton, marker configuration and motion, and to learn to predict these latent vectors from imperfect marker sequences containing noise and errors. By decoding these components from latent vectors, sequences of clean markers and skeletons can be directly recovered. Moreover, we also provide a novel normalization strategy based on learning a pose-dependent marker reliability function, which greatly improves system robustness. Experimental results demonstrate that our algorithm consistently outperforms the state-of-the-art on both synthetic and real-world datasets.

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The thermal and mechanical properties of two types of polyester nanofiber, poly (1,4-cyclohexanedimethylene isosorbide terephthalate) (PICT) copolymers and the terpolyester of isosorbide, ethylene glycol, 1,4-cyclohexane dimethanol, and terephthalic acid (PEICT), were investigated. This is the first attempt to fabricate PICT nanofiber via the electrospinning method; comparison with PEICT nanofiber could give greater understanding of eco-friendly nanofibers containing biomass monomers. The nanofibers fabricated from each polymer show similar smooth and thin-and-long morphologies. On the other hand, the polymers exhibited significantly different mechanical and thermal properties; in particular, a higher tensile strength was observed for PICT nanofiber mat than for that of PEICT. We hypothesized that PICT has more trans-configuration than PEICT, resulting in enhancement of its tensile strength, and demonstrated this by Fourier transform infrared spectroscopy. In addition, PICT nanofibers showed clear crystallization behavior upon increased temperature, while PEICT nanofibers showed completely amorphous structure. Both nanofibers have better tensile properties and thermal stability than the typical polyester polymer, implying that they can be utilized in various industrial applications.

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