Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow

• URL: http://arxiv.org/abs/2310.14780v1
• Date: Fri, 20 Oct 2023 12:53:08 GMT
• Title: Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow
• Authors: Haipeng Fang, Zhihao Sun, Ziyao Huang, Fan Tang, Juan Cao, Sheng Tang
• Abstract summary: We present Dance--Latents, a framework that makes latents dance coherently following motion flow to generate consistent dance videos. Experimental results in TikTok dataset demonstrate that our approach significantly enhancestemporal consistency of irregular generated videos.
• Score: 22.1733448870831
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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