The advancement of generative AI has extended to the realm of Human Dance
Generation, demonstrating superior generative capacities. However, current
methods still exhibit deficiencies in achieving spatiotemporal consistency,
resulting in artifacts like ghosting, flickering, and incoherent motions. In
this paper, we present Dance-Your-Latents, a framework that makes latents dance
coherently following motion flow to generate consistent dance videos. Firstly,
considering that each constituent element moves within a confined space, we
introduce spatial-temporal subspace-attention blocks that decompose the global
space into a combination of regular subspaces and efficiently model the
spatiotemporal consistency within these subspaces. This module enables each
patch pay attention to adjacent areas, mitigating the excessive dispersion of
long-range attention. Furthermore, observing that body part's movement is
guided by pose control, we design motion flow guided subspace align & restore.
This method enables the attention to be computed on the irregular subspace
along the motion flow. Experimental results in TikTok dataset demonstrate that
our approach significantly enhances spatiotemporal consistency of the generated
videos.

Dance-Your-Latents 是一个生成一致舞蹈视频的框架，通过引入空间 - 时间子空间 - 注意力块，模拟了每个元素在有限空间内的运动，以实现生成视频的时空一致性，并通过姿态控制指导下的运动流量对其进行补全和对齐，实验结果表明该方法显著提升了生成视频的时空一致性。