基于跨模态注意力与可微分哈希的运动-文本双向检索框架OA

The rapid development of the 3D animation,film and television production,and gaming industries has accumulated massive amounts of high-precision 3D human motion data,making efficient management and intelligent retrieval a serious challenge.In response to the two major bottlenecks in current cross modal retrieval research-insufficient modeling of the association between human motion and text semantics and high computational costs,this paper proposes a differentiable hash cross modal retrieval framework based on attention fusion.It innovatively constructs a dual channel Transformer framework to achieve feature extraction of motion capture data and natural language,captures fine-grained spatiotemporal correlations between motion sequences and text descriptions with learnable cross modal attention mechanisms,and designs an end-to-end hash encoding optimization strategy to compress high-dimensional features into compact binary streams.Experiments have shown that this method significantly improves the accuracy and efficiency of bidirectional motion text retrieval on commonly used datasets.Compared to the baseline model,the sum of recall rates has increased by 2.6 times,providing an efficient solution for motion data reuse in fields such as digital entertainment.