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.
Research paths
How this paper contributes to the site's broader research map.
- Semantic Motion and Embodied InteractionFoundation
SEEG separates rhythmic and semantic cues in co-speech gesture, establishing that synchronized motion is not automatically communicative motion.
Research question
Co-speech gesture models can learn rhythmic alignment while failing to explicitly capture and express the semantic content conveyed by meaningful gestures.
What the paper contributes
- 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 and evaluation scope
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.
Scope and 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 for related work
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.
Related-work context
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.
A concise, neutral description of how this paper can be situated in related work.
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.
The abstract is reproduced for scholarly identification and remains under the paper publisher/authors’ original copyright; it is not covered by this page’s CC BY license.
Evidence references
| What to verify | Location in the paper |
|---|---|
| Problem statement | Abstract |
| Method and contributions | Abstract; DEcoupled Mining and Semantic Energizing Module sections |
| Evaluation statement | Abstract; semantic-aware quantitative and qualitative evaluations |
Primary source: IEEE version of record (CVPR 2022 version of record; CVF open-access copy has alternate pagination 10473-10482).
How to cite
Cite the paper—not this explainer—for scientific claims. Cite this page only when reusing its original commentary.
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.
Reuse policy
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