TVTA Trajectory-Aware Viseme-Guided Temporal Aggregation for Event-Based Lip Reading

Jul 16, 2026

Event-based lip reading enables visual speech recognition using event cameras’ microsecond temporal resolution, motion sensitivity, and sparse event streams, helping in noisy and privacy-sensitive settings. Current approaches often compress spatial information before sufficient temporal modeling, weakening sparse, localized lip motion trajectories, and optimize mainly for word-classification with weak constraints on articulatory structure. The proposed temporally enhanced framework introduces trajectory-aware differential aggregation, viseme-guided temporal supervision, and EMA teacher–student training, validated on the DVS-Lip benchmark with ablations and qualitative decoding.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

Introduction

Motivation and limitations of existing methods

Proposed temporally enhanced framework

Key components: TDA, VGA, and teacher–student training

arXiv :2607 .08236v 1 [ cs .CV] 9 Jul 2026 TVTA: Trajectory-Aware Viseme-Guided Temporal Aggregation for Event-Based Lip Reading JINGRONG ZHENG, Harbin Institute of Technology, China HONGWEI REN∗ , Harbin Institute of Technology, China XIANGQIAN WU, Harbin Institute of Technology, China Event-based lip reading has recently emerged as a promising direction for visual speech recognition, benefiting from the high temporal resolution and motion sensitivity of event cameras. However, existing methods typically perform spatial compression before sufficient temporal modeling, which may suppress sparse and localized motion trajectories that are crucial for distinguishing similar lip movements. Moreover, most current approaches optimize temporal representations mainly at the word-classification level, leaving the underlying articulatory structure weakly constrained. To address these limitations, we propose a temporally enhanced framework for event-based lip reading. First, we introduce Trajectory-Aware Differential Aggregation (TDA), which performs local temporal modeling at each spatial location before adaptive spatial aggregation. Second, we propose Viseme-Guided Aggregation (VGA), a unified temporal module composed of a CTC decoder and aviseme-guided gated aggregation branch, which injects viseme-aware sequence supervision and improves final temporal aggregation for word recognition. Third, we incorporate an EMA teacher–student training strategy to enhance robustness under strong event perturbations. Experiments on the DVS-Lip benchmark verify the effectiveness of the proposed design, and extensive ablation studies further validate the contributions of TDA, VGA, and teacher–student consistency. Qualitative decoding results also demonstrate that the proposed CTC-based temporal modeling learns meaningful viseme-aware structure from event streams. Additional Key Words and Phrases: Event-based lip reading, event camera, temporal aggregation, viseme supervision 1 Introduction Lip reading aims to infer spoken content from visual articulations and has become an important complement to automatic speech recognition, particularly in noisy environments and privacysensitive scenarios where acoustic signals are unreliable or unavailable. Compared with conventional frame-based cameras, event cameras asynchronously record brightness changes with microsecond temporal resolution, high dynamic range, and sparse event streams, making them particularly suitable for capturing subtle and rapidly evolving lip motions. These characteristics enable event cameras to preserve fine-grained visual articulations while reducing redundant appearance information, providing a promising sensing modality for visual speech recognition. Despite these advantages, effectively modeling event-based visual speech remains challenging. Unlike global facial movements, discriminative lip-reading cues are often encoded in subtle and localized articulatory patterns that evolve continuously over time. Consequently, learning temporal representations that simultaneously preserve fine-grained local dynamics and high-level articulation structures remains a fundamental challenge for event-based lip reading. Driven by these advantages, event-based lip reading has recently attracted growing attention. The early MSTP framework [34] demonstrated that multigrained spatio-temporal representations can effectively model event lip streams, and its subsequent extension MSTP++ [33] further refined this design line. Later work explored complementary directions, including temporal granularity alignment [45], event-specific triplane motion analysis [21], state-space temporal modeling [48], ∗ Corresponding author. Authors’ Contact Information: Jingrong Zheng, Harbin Institute of Technology, Harbin, China, [2023113392@stu.hit.edu](2023113392@stu.hit.edu). cn; Hongwei Ren, Harbin Institute of Technology, Harbin, China, [ww@xxx.edu.cn](ww@xxx.edu.cn); Xiangqian Wu, Harbin Institute of T

FAQs

Why is event-based lip reading considered promising?

Event cameras asynchronously capture brightness changes with microsecond temporal resolution and strong motion sensitivity, preserving subtle lip articulations while reducing redundant appearance information.

What limitations do existing event-based lip reading methods have?

They often perform spatial compression before adequate temporal modeling, which can suppress sparse localized motion trajectories, and they largely optimize temporal representations at the word-classification level with weak constraints on articulatory structure.

How does the proposed framework improve temporal modeling and robustness?

It introduces Trajectory-Aware Differential Aggregation (local temporal modeling before adaptive spatial aggregation), Viseme-Guided Aggregation with CTC-based supervision and a viseme-guided gated branch, and an EMA teacher–student training strategy to enhance robustness under event perturbations.

Jul 16, 2026

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