Paper:
Learning diverse and natural behaviors is one of the longstanding goal for creating intelligent characters in the animated world. In this paper, we propose "COmposable Semi-parametric MOdelling" (COSMO), a method for generating long range diverse and distinctive behaviors to achieve a specific goal location. Our proposed method learns to model the motion of human by combining the complementary strengths of both non-parametric techniques and parametric ones. Given the starting and ending state, a memory bank is used to retrieve motion references that are provided as source material to a deep network. The synthesis is performed by the deep network that controls the style of the provided motion material and modifies it to become natural. On skeleton datasets with diverse motion, we show that the proposed method outperforms existing parametric and non-parametric baselines. We also demonstrate the generated sequences are useful as subgoals for actual physical execution in the animated world. Please refer to our project page 1 for more synthesised results.

Review:
This paper aims at taking techniques from motion interpolation into the regime where one is able to generate longer range motion sequences, in the domain of physically plausible computer animation of characters. In the way that the authors have set up the problem, an initial database seeds the search for plausible transitions between two given poses. So, the technique being proposed must address how to keep physical realism (the long-standing question of "dynamics filtering" along the lines of Yamane, Katsu, and Yoshihiko Nakamura. "Dynamics filter-concept and implementation of online motion generator for human figures." IEEE transactions on robotics and automation 19.3 (2003): 421-432. Of course the problem here also needs to address "style" which needs different models). 
The authors propose an approach, building on recent neural network architectures including GANs and VAEs, which combines an embedding into a latent space to capture the style+content and then a Bi-LSTM model to make P-step predictions to extract the interpolating poses. The architecture is not very well explained and could be presented much better. However, the constituent elements are fairly standard ones. The learning of the embedding space is based on minimising a reconstruction loss and adapting parameters of a network that takes convolutions over time windows and stacks different channels. The LSTM training is also based on prior work in that domain. 
The authors try to show with experiments that the proposed model is doing better both on diversity and accuracy in standard motion capture datasets. However, the baselines used, especially for the accuracy comparison, are more along the lines of an ablation study than a genuine comparison with alternate methods.
This is coupled with the fact that the authors do not seem to have engaged with a fairly established literature on modelling such sequences, e.g., 
Brand, M., & Hertzmann, A. (2000, July). Style machines. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques (pp. 183-192). ACM Press/Addison-Wesley Publishing Co..
Li, Y., Wang, T., & Shum, H. Y. (2002, July). Motion texture: a two-level statistical model for character motion synthesis. In ACM transactions on graphics (ToG) (Vol. 21, No. 3, pp. 465-472). ACM.
These papers also solve the same problem the authors have set out to solve and arguably do pretty well. How well do they compare to the authors' approach and in what ways have they built further? It would be helpful to understand this with quantitative evidence. 
As it stands, this comes across as a report on a preliminary experiment. Indeed, figure 10 is just a single learning curve without much more interpretation and analysis. The paper would be much stronger if situated better with respect to other established work and also supported by more systematic empirical experiments.