SemanticBoost: Elevating Motion Generation with Augmented Textual Cues

Xin He, Shaoli Huang, Xiaohang Zhan, CHAO WENG, Ying Shan

Keywords: Motion Synthesis; Semantic alignment; Data Augmentation; Denoise Network

TL;DR: We propose a text-to-motion framework which has better semantic alignment and consists of network optimization and dataset optimization.

Abstract: In the rapidly advancing domain of motion generation, enhancing textual semantics has been recognized as a highly promising strategy for producing more accurate and realistic motions. However, current techniques frequently depend on extensive language models to refine text descriptions, without guaranteeing precise alignment between textual and motion data. This misalignment often leads to suboptimal motion generation, limiting the potential of these methods. To address this issue, we introduce a novel framework called SemanticBoost, which aims to bridge the gap between textual and motion data. Our innovative solution integrates supplementary semantic information derived from the motion data itself, along with a dedicated denoise network, to guarantee semantic coherence and elevate the overall quality of motion generation. Through extensive experiments and evaluations, we demonstrate that SemanticBoost significantly outperforms existing methods in terms of motion quality, alignment, and realism. Moreover, our findings emphasize the potential of leveraging semantic cues from motion data, opening new avenues for more intuitive and diverse motion generation.

Primary Area: generative models

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Submission Number: 2236