SICAGE: Speaker-Independent Culture-Aware Gesture Generation using TED4C-L Dataset
Abstract
Recent co-speech gesture generation methods often overlook cultural differences, limiting their effectiveness in human-agent interaction. Moreover, culture-conditioned models are rarely evaluated under speaker-disjoint splits, so apparent "cultural" behavior may be confounded with speaker-specific gesturing style. We introduce SICAGE, a modular framework for culture-aware co-speech gesture generation that conditions motion synthesis models on speaker-independent cultural representations. SICAGE learns these representations from audio and text by treating each speaker as a separate domain while imposing invariance across speakers. This encourages representations to remain culture-discriminative while reducing dependence on speaker identity. The resulting cultural embeddings condition a multimodal generator to produce culturally appropriate gestures. We instantiate this idea with two domain generalization approaches: adversarial learning and Fishr regularization. We further introduce ALaDiT, a real-time diffusion-based gesture generator designed to efficiently incorporate the learned cultural embeddings. To validate our method, we built TED4C-L, a 106-hour multimodal dataset of 764 TED speakers from four cultural groups. Experiments show that SICAGE improves motion realism, diversity, beat synchronization, semantic relevance, and cultural consistency.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a well-motivated and technically solid approach to culture-aware gesture generation, introducing a new dataset and a novel method for disentangling cultural style from speaker identity, which addresses a critical gap in the field of multimodal gesture synthesis.
Methodology
The paper proposes SICAGE, a framework for culture-aware co-speech gesture generation. The core methodological contribution is the decoupling of cultural style from speaker identity. This is achieved by treating each speaker as a separate domain and employing domain generalization techniques (Fishr regularization and adversarial learning) to learn speaker-invariant cultural embeddings from audio and text. These embeddings then condition a diffusion-based generator, ALaDiT. The approach is theoretically sound, addressing a specific confound in existing literature (speaker-specific style vs. cultural style). The use of Fishr for gradient variance matching is a novel application in this specific context, though domain generalization itself is a well-established paradigm. The generator architecture (ALaDiT) is a standard diffusion transformer with multimodal conditioning, which is less novel than the representation learning component.
Results & evaluation
The authors introduce TED4C-L, a new dataset of 106 hours of TED talks from four cultural groups. This is a significant contribution to the data landscape for this niche. The evaluation includes standard gesture metrics (FGD, SRGR, BAS, Diversity) and a novel "Cultural Expressivity" (CE) metric using a speaker-disjoint classifier. The ablation studies are thorough, comparing Fishr, Adversarial, OneHot, and NoDG variants. The results show that explicit cultural conditioning improves performance, particularly with Fishr. The user study provides qualitative validation, though with a small sample size (N=20). The statistical significance is reported, but the effect sizes in some metrics are modest. The comparison with baselines (MDM, DSG+) is fair and demonstrates the superiority of the proposed method.
Reproducibility
The paper provides detailed descriptions of the dataset construction, model architectures, and training hyperparameters. The code and dataset are made available via the project URL. The use of standard libraries (PyTorch, MMPose, wav2vec) and clear loss function definitions enhances reproducibility. The speaker-disjoint split strategy is clearly defined.
Limitations
The cultural grouping is coarse (country-level), which may not capture intra-cultural diversity or nuances. The motion representation is limited to 9 upper-body keypoints, excluding hands and lower body, which limits the expressiveness of the generated gestures. The user study sample size is small, and the participants' familiarity with the specific cultures is not detailed, potentially biasing the "cultural fit" ratings. The reliance on monocular pose estimation introduces noise, particularly for the Japanese subset where visibility was lower.
Broader impact
This work has significant implications for creating more natural and culturally sensitive human-agent interactions, social robots, and virtual avatars. By explicitly modeling cultural differences, it promotes inclusivity in AI systems. However, there is a risk of reinforcing stereotypes if the "cultural" patterns learned are overly simplistic or biased. The dataset release contributes to the community but requires careful handling to avoid misuse. The paper presents a well-motivated and technically solid approach to culture-aware gesture generation, introducing a new dataset and a novel method for disentangling cultural style from speaker identity, which addresses a critical gap in the field of multimodal gesture synthesis.
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