# Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images
## 1 Introduction  
Human movement generation, although less developed than for instance text generation, is an important applicative field for sequential data generation. It is difficult to get enough effective human movement dataset due to its complexity--human movement is the result of both physical limitation (torque exerted by muscles, gravity, and moment preservation) and the intentions of subjects (how to perform an intentional motion) [1]. Generating artificial human movement data enables data augmentation, which should improve the performance of models in all fields of study on human movement: classification, prediction, generation, etc. Also, it will have important applications in related domains. Imagine, for example, in computer games, each character running and jumping in exactly the same way but in their own style, making those actions in the game closer to reality. The objective of this research is to develop a generative model for skeletal human movements, allowing to control the action type of generated motion while keeping the authenticity of the result and the natural style variability of gesture execution. Skeleton-based movement generation has recently become an active topic in computer vision, owing to the potential advantages of skeletal representationand to recent algorithms for automatically extracting skeletal pose sequences from videos. Previously, most of the methods and researches on human gestures, including human movement generation, used RGB image sequences, depth image sequences, videos, or some fusion of these modalities as input data. Nowadays, it is easy to get relatively accurate 2D or 3D human skeletal pose data thanks to state-of-the-art human pose estimation algorithms and high-performance sensors. Compared to dense image approaches, skeletal data has the following advantages: (a) lower computational requirement and (b) robustness under situations such as complicated background and changing body scales, viewpoints, motion speed, etc. Besides these advantages, skeletal data has the three following main characteristics [2]:  
* spatial information: strong correlations between adjacent joints, which makes it possible to learn about body structural information within a single frame (intra-frame);
* temporal information: makes it possible to learn about temporal correlation between frames (inter-frame); and
* cooccurrence relationship between spatial and temporal domains when taking joints and bones into account. Skeletal-based human movement generation can be formalized as a continuous multivariate time-series problem and is therefore often tackled using Recurrent Neural Network (RNN). In our work, we however decided to use representation of each skeletal pose sequence as a spatiotemporal pseudo-image with space and time as two dimensions of the image instead. The advantage is that we can directly apply state-of-the-art generative models for images, which are more mature and simpler in terms of the network architecture than the ones tailored for continuous time-series generation. Thus, it is important to find a good pseudo-image representation for 3D skeletal data. The related works will be further developed in the next part. The key issues to be solved include how to generate realistic human movements, how to control the output (action, style, etc. ), and how to evaluate the model effectively. Our work proves that, as long as we apply a well-adapted pseudo-image representation for 3D skeletal data, such as Tree Structure Skeleton Image (TSSI), we can generate human movement by a simple standard image generative model such as Deep Convolutional Generative Adversarial Network (DC-GAN [3]). Furthermore, by introducing a conditional label, we control the output action type. We also evaluate our generative models by calculating Frechet inception distance.