论文概要
SEEG 先用 DEcoupled Mining module 分离节奏相关与语义相关信息，再通过 Semantic Energizing Module 和 semantic prompter，使生成的 co-speech gesture 不只对齐语音节奏，也表达相应语义。
研究问题
Co-speech gesture 模型容易学到语音节奏，却难以显式捕获并表达有意义手势所承载的语义。
论文贡献
- 提出 DEcoupled Mining（DEM），分别挖掘 beat gesture 与 semantic gesture 信息。
- 提出 Semantic Energizing Module（SEM），不仅约束表示相似，还监督语义表达。
- 使用 semantic prompter 将语义感知监督传递给生成动作。
证据与评测范围
论文在多个 benchmark 和三项指标上评测，并报告 semantic-aware evaluation 与定性表现的改进；准确指标和数据集结果应引用 CVPR 正式论文。
适用范围与局限
SEM 能表达哪些语义取决于可用语义类别和监督。方法并不意味着已覆盖所有文化相关手势语义或开放域交流意图。
Related work 定位
SEEG 是 semantic-aware co-speech gesture 方法，显式拆分易学的节奏线索与更难的语义线索，并为语义表达加入专门监督。
Related Work 表述
Liang 等提出 SEEG，解耦节奏与语义手势线索，并通过 Semantic Energizing Module 的语义感知监督生成 co-speech gesture。
这是一段用于说明论文定位的简洁中性表述。
论文官方英文摘要
Talking gesture generation is a practical yet challenging task which aims to synthesize gestures in line with speech. Gestures with meaningful signs can better convey useful information and arouse sympathy in the audience. Current works focus on aligning gestures with the speech rhythms, which are hard to mine the semantics and model semantic gestures explicitly. In this paper, we propose a novel method SEmantic Energized Generation (SEEG), for semantic-aware gesture generation. Our method contains two parts: DEcoupled Mining module (DEM) and Semantic Energizing Module (SEM). DEM decouples the semantic-irrelevant information from inputs and separately mines information for the beat and semantic gestures. SEM conducts semantic learning and produces semantic gestures. Apart from representational similarity, SEM requires the predictions to express the same semantics as the ground truth. Besides, a semantic prompter is designed in SEM to leverage the semantic-aware supervision to predictions. This promotes the networks to learn and generate semantic gestures. Experimental results reported in three metrics on different benchmarks prove that SEEG efficiently mines semantic cues and generates semantic gestures. In comparison, SEEG outperforms other methods in all semantic-aware evaluations on different datasets. Qualitative evaluations also indicate the superiority of SEEG in semantic expressiveness.
摘要仅用于学术识别，版权仍归论文作者或出版方所有，不属于本页 CC BY 许可范围。
依据与出处
| 核对内容 | 论文中的位置 |
|---|---|
| 问题陈述 | Abstract |
| 方法与贡献 | Abstract; DEcoupled Mining and Semantic Energizing Module sections |
| 评测结论 | Abstract; semantic-aware quantitative and qualitative evaluations |
主要核验来源: IEEE version of record (CVPR 2022 version of record; CVF open-access copy has alternate pagination 10473-10482).
如何引用
科研结论应引用论文本身；只有在复用本站原创解读时才引用本页。
Yuanzhi Liang, Qianyu Feng, Linchao Zhu, Li Hu, Pan Pan, and Yi Yang. “SEEG: Semantic Energized Co-speech Gesture Generation.” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), 10463-10472. https://doi.org/10.1109/CVPR52688.2022.01022.
复用许可
本页原创解读采用 CC BY 4.0：复用时须署名并链接本页。论文标题、摘要、图表和书目信息不在此许可范围内，仍保留原有权利。 Creative Commons Attribution 4.0 International.