  Existing keyframe-based motion synthesis mainly focuses on the generation of
cyclic actions or short-term motion, such as walking, running, and transitions
between close postures. However, these methods will significantly degrade the
naturalness and diversity of the synthesized motion when dealing with complex
and impromptu movements, e.g., dance performance and martial arts. In addition,
current research lacks fine-grained control over the generated motion, which is
essential for intelligent human-computer interaction and animation creation. In
this paper, we propose a novel keyframe-based motion generation network based
on multiple constraints, which can achieve diverse dance synthesis via learned
knowledge. Specifically, the algorithm is mainly formulated based on the
recurrent neural network (RNN) and the Transformer architecture. The backbone
of our network is a hierarchical RNN module composed of two long short-term
memory (LSTM) units, in which the first LSTM is utilized to embed the posture
information of the historical frames into a latent space, and the second one is
employed to predict the human posture for the next frame. Moreover, our
framework contains two Transformer-based controllers, which are used to model
the constraints of the root trajectory and the velocity factor respectively, so
as to better utilize the temporal context of the frames and achieve
fine-grained motion control. We verify the proposed approach on a dance dataset
containing a wide range of contemporary dance. The results of three
quantitative analyses validate the superiority of our algorithm. The video and
qualitative experimental results demonstrate that the complex motion sequences
generated by our algorithm can achieve diverse and smooth motion transitions
between keyframes, even for long-term synthesis.
