Ferdinand Paar, Lanmiao Liu, Aslı Özyürek, Serge Thill, Esam Ghaleb
Co-speech gesture generation requires both semantic expressivity and biomechanically plausible rhythmic motion. Existing holistic gesture models mix lexically grounded semantic gestures with frequent prosody-aligned beat gestures. This limits semantic grounding, speech-motion alignment, and kinematic smoothness. We propose DuoGesture, a neuro-inspired and biomechanically informed approach that decomposes co-speech gesture synthesis into semantic and beat streams. The two streams are coordinated by a Semantic Variational Information Bottleneck, a stochastic frame-level gate that learns when semantic gestures should override rhythmic beat motion. The semantic stream is controlled by Motion-Grounded Semantic Conditioning, which replaces purely linguistic word embeddings with motion-language representations to provide motion-aligned semantic priors for long-tailed lexical triggers of gestures. The beat stream is further regularised by an Inertial Beat Prior, an anthropometry-weighted arm-chain module that reduces jitter and improves rhythmic consistency without constraining semantic frames. Objective evaluations and subjective experiments show that DuoGesture outperforms strong baselines, while component ablations confirm the complementary roles of semantic grounding, stochastic stream selection, and biomechanical regularisation. We encourage readers to view our demos and qualitative illustrations, which are available on the project page: https://ferdinandpaar.github.io/DuoGesture/.
Onur Keleş Bogazici University, Asli Ozyurek mpi for psycholinguistics, Gerardo Ortega, Kadir Gökgöz Bogazici University, Esam Ghaleb The Max Planck Institute for Psycholinguistics
Iconicity, the resemblance between linguistic form and meaning, is pervasive in sign languages, offering a natural testbed for visual grounding in vision–language models (VLMs). We introduce the Visual Iconicity Challenge, a video-based benchmark that adapts psycholinguistic measures to evaluate VLMs on three tasks: (i) phonological sign-form prediction, (ii) transparency (inferring meaning from visual form), and (iii) graded iconicity ratings. We assess 17 state-of-the-art VLMs in zero- and few-shot settings on Sign Language of the Netherlands and compare them to human baselines. VLMs mirror human phonological difficulty patterns (e.g., handshape harder than location) and achieve moderate to strong alignment with human iconicity ratings. However, they still fail to infer lexical meaning from visual form alone and show a systematic object-based bias that inverts the human preference for action-based signs. Crucially, models with stronger phonological form prediction correlate better with human iconicity judgments, indicating shared sensitivity to visually grounded structure. Our findings validate these diagnostic tasks, show that explicit reasoning narrows the open-to-closed-model calibration gap, and motivate human-centric signals for modelling iconicity in multimodal models.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表