Adaptive Compact Attention For Few-shot Video-to-video Translation

• URL: http://arxiv.org/abs/2011.14695v1
• Date: Mon, 30 Nov 2020 11:19:12 GMT
• Title: Adaptive Compact Attention For Few-shot Video-to-video Translation
• Authors: Risheng Huang, Li Shen, Xuan Wang, Cheng Lin, Hao-Zhi Huang
• Abstract summary: We introduce a novel adaptive compact attention mechanism to efficiently extract contextual features jointly from multiple reference images. Our core idea is to extract compact basis sets from all the reference images as higher-level representations. We extensively evaluate our method on a large-scale talking-head video dataset and a human dancing dataset.
• Score: 13.535988102579918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes an adaptive compact attention model for few-shot video-to-video translation. Existing works in this domain only use features from pixel-wise attention without considering the correlations among multiple reference images, which leads to heavy computation but limited performance. Therefore, we introduce a novel adaptive compact attention mechanism to efficiently extract contextual features jointly from multiple reference images, of which encoded view-dependent and motion-dependent information can significantly benefit the synthesis of realistic videos. Our core idea is to extract compact basis sets from all the reference images as higher-level representations. To further improve the reliability, in the inference phase, we also propose a novel method based on the Delaunay Triangulation algorithm to automatically select the resourceful references according to the input label. We extensively evaluate our method on a large-scale talking-head video dataset and a human dancing dataset; the experimental results show the superior performance of our method for producing photorealistic and temporally consistent videos, and considerable improvements over the state-of-the-art method.

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