{
  "schema_version": 1,
  "slug": "seeg",
  "canonical_url": "https://akira-l.github.io/publications/seeg/",
  "language_urls": {
    "en": "https://akira-l.github.io/publications/seeg/",
    "zh-CN": "https://akira-l.github.io/zh/publications/seeg/"
  },
  "title": "SEEG: Semantic Energized Co-speech Gesture Generation",
  "short_title": "SEEG",
  "authors": [
    "Yuanzhi Liang",
    "Qianyu Feng",
    "Linchao Zhu",
    "Li Hu",
    "Pan Pan",
    "Yi Yang"
  ],
  "publication": {
    "kind": "conference",
    "venue": "IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)",
    "citation_container_title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
    "status": "published",
    "year": 2022,
    "publication_date": "2022",
    "publisher": "IEEE",
    "pages": "10463-10472",
    "alternate_pagination": {
      "pages": "10473-10482",
      "version": "CVF open-access copy",
      "url": "https://openaccess.thecvf.com/content/CVPR2022/html/Liang_SEEG_Semantic_Energized_Co-Speech_Gesture_Generation_CVPR_2022_paper.html"
    }
  },
  "identifiers": {
    "doi": "10.1109/CVPR52688.2022.01022"
  },
  "arxiv_dates": {},
  "keywords": [
    "co-speech gesture",
    "gesture generation",
    "semantic gestures",
    "speech rhythm",
    "disentangled learning"
  ],
  "official_abstract": "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.",
  "official_abstract_rights": "Excluded from the site's CC BY 4.0 license; original paper rights apply.",
  "primary_source": {
    "label": "IEEE version of record",
    "url": "https://doi.org/10.1109/CVPR52688.2022.01022",
    "version": "CVPR 2022 version of record; CVF open-access copy has alternate pagination 10473-10482",
    "method_locator": "Abstract; DEcoupled Mining and Semantic Energizing Module sections",
    "evidence_locator": "Abstract; semantic-aware quantitative and qualitative evaluations"
  },
  "source_checked": "2026-07-31",
  "verification_status": "author-verified",
  "author_verified_on": "2026-07-31",
  "commentary": {
    "license": "https://creativecommons.org/licenses/by/4.0/",
    "en": {
      "summary": "SEEG separates beat-related and semantic information with a DEcoupled Mining module, then uses a Semantic Energizing Module and semantic prompter to make generated co-speech gestures express semantics as well as align with speech.",
      "problem": "Co-speech gesture models can learn rhythmic alignment while failing to explicitly capture and express the semantic content conveyed by meaningful gestures.",
      "contributions": [
        "Introduces DEcoupled Mining (DEM) to separate information for beat gestures and semantic gestures.",
        "Introduces a Semantic Energizing Module (SEM) that supervises semantic expression, not only representation similarity.",
        "Uses a semantic prompter to transfer semantic-aware supervision to generated gestures."
      ],
      "evidence": "The paper reports results on multiple benchmarks and three metrics, with gains on semantic-aware evaluations plus qualitative improvements in expressiveness. Exact metrics and dataset results should be cited from the CVPR paper.",
      "limitations": "The semantic categories and supervision available to SEM shape what counts as expressive meaning. The method does not imply that all culturally dependent gesture semantics or open-domain communicative intent are captured.",
      "positioning": "SEEG is a semantic-aware co-speech gesture method that explicitly separates easier rhythmic cues from harder semantic cues and adds supervision for semantic expression.",
      "citation_ready": "Liang et al. propose SEEG, which decouples beat and semantic gesture cues and applies semantic-aware supervision through a Semantic Energizing Module for co-speech gesture generation."
    },
    "zh-CN": {
      "summary": "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。",
      "problem": "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。",
      "contributions": [
        "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。"
      ],
      "evidence": "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。",
      "limitations": "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。",
      "positioning": "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。",
      "citation_ready": "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。"
    }
  },
  "citation": {
    "id": "liang2022seeg",
    "type": "paper-conference",
    "title": "SEEG: Semantic Energized Co-speech Gesture Generation",
    "author": [
      {
        "given": "Yuanzhi",
        "family": "Liang"
      },
      {
        "given": "Qianyu",
        "family": "Feng"
      },
      {
        "given": "Linchao",
        "family": "Zhu"
      },
      {
        "given": "Li",
        "family": "Hu"
      },
      {
        "given": "Pan",
        "family": "Pan"
      },
      {
        "given": "Yi",
        "family": "Yang"
      }
    ],
    "container-title": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
    "issued": {
      "date-parts": [
        [
          2022
        ]
      ]
    },
    "URL": "https://doi.org/10.1109/CVPR52688.2022.01022",
    "abstract": "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.",
    "keyword": "co-speech gesture, gesture generation, semantic gestures, speech rhythm, disentangled learning",
    "publisher": "IEEE",
    "DOI": "10.1109/CVPR52688.2022.01022",
    "page": "10463-10472"
  },
  "resources": [
    {
      "label": "Version of record",
      "url": "https://doi.org/10.1109/CVPR52688.2022.01022"
    },
    {
      "label": "Open access",
      "url": "https://openaccess.thecvf.com/content/CVPR2022/html/Liang_SEEG_Semantic_Energized_Co-Speech_Gesture_Generation_CVPR_2022_paper.html"
    },
    {
      "label": "PDF",
      "url": "https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_SEEG_Semantic_Energized_Co-Speech_Gesture_Generation_CVPR_2022_paper.pdf"
    }
  ]
}