Abstract
This paper presents techniques to animate realistic human-like motion using a compressed learnt model from 4D volumetric performance capture data. Sequences of 4D dynamic geometry representing a human performing an arbitrary motion are encoded through a generative network into a compact space representation, whilst maintaining the original properties, such as, surface dynamics. An animation framework is proposed which computes an optimal motion graph using the novel capabilities of compression and generative synthesis properties of the network. This approach significantly reduces the memory space requirements, improves quality of animation, and facilitates the interpolation between motions. The framework optimises the number of transitions in the graph with respect to the shape and motion of the dynamic content. This generates a compact graph structure with low edge connectivity, and maintains realism when transitioning between motions. Finally, it demonstrates that generative networks facilitate the computation of novel poses, and provides a compact motion graph representation of captured dynamic shape enabling real-time interactive animation and interpolation of novel poses to smoothly transition between motions.