# MM-Gesture: Towards Precise Micro-Gesture Recognition through Multimodal Fusion

arXiv: https://arxiv.org/abs/2507.08344 · HTML: https://mlsanity.com/paper/2507.08344/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [Video Depth Anything: Consistent Depth Estimation for Super-Long Videos](https://mlsanity.com/paper/2501.12375/) (2025)
- [iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis](https://mlsanity.com/paper/2107.00285/) (2021)
- [MemFlow: Optical Flow Estimation and Prediction with Memory](https://mlsanity.com/paper/2404.04808/) (2024)
- [Benchmarking Micro-action Recognition: Dataset, Methods, and Applications](https://mlsanity.com/paper/2403.05234/) (2024)
- [MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs](https://mlsanity.com/paper/2410.12332/) (2024)
- [FROSTER: Frozen CLIP Is A Strong Teacher for Open-Vocabulary Action Recognition](https://mlsanity.com/paper/2402.03241/) (2024)
- [Hierarchical Contrast for Unsupervised Skeleton-based Action Representation Learning](https://mlsanity.com/paper/2212.02082/) (2022)
- [Frequency Decoupling for Motion Magnification via Multi-Level Isomorphic Architecture](https://mlsanity.com/paper/2403.07347/) (2024)
- [EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer](https://mlsanity.com/paper/2312.04152/) (2023)
- [Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification](https://mlsanity.com/paper/2307.10624/) (2023)
- [Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition](https://mlsanity.com/paper/2412.14719/) (2024)
- [Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding](https://mlsanity.com/paper/2311.03106/) (2023)
- [MMAD: Multi-label Micro-Action Detection in Videos](https://mlsanity.com/paper/2407.05311/) (2024)
- [Bodily Behaviors in Social Interaction: Novel Annotations and State-of-the-Art Evaluation](https://mlsanity.com/paper/2207.12817/) (2022)
- [Prototype Learning for Micro-gesture Classification](https://mlsanity.com/paper/2408.03097/) (2024)
- [Data Augmentation for Human Behavior Analysis in Multi-Person Conversations](https://mlsanity.com/paper/2308.01526/) (2023)
- [Taylor Videos for Action Recognition](https://mlsanity.com/paper/2402.03019/) (2024)
- [Temporal-Frequency State Space Duality: An Efficient Paradigm for Speech Emotion Recognition](https://mlsanity.com/paper/2412.16904/) (2024)
- [Exploiting Ensemble Learning for Cross-View Isolated Sign Language Recognition](https://mlsanity.com/paper/2502.02196/) (2025)
- [DEEMO: De-identity Multimodal Emotion Recognition and Reasoning](https://mlsanity.com/paper/2504.19549/) (2025)
- [Towards Fine-Grained Emotion Understanding via Skeleton-Based Micro-Gesture Recognition](https://mlsanity.com/paper/2506.12848/) (2025)

## Built on by (most cited first, 14 of 14)

- [Motion Matters: Motion-guided Modulation Network for Skeleton-based Micro-Action Recognition](https://mlsanity.com/paper/2507.21977/) (2025)
- [MA-Bench: Towards Fine-grained Micro-Action Understanding](https://mlsanity.com/paper/2603.26586/) (2026)
- [Spatial-Temporal Decoupled Adapter for Micro-gesture Online Recognition](https://mlsanity.com/paper/2606.07355/) (2026)
- [Self-supervised Learning Matters: A Simple Ensemble Solution for Micro-Gesture Recognition](https://mlsanity.com/paper/2606.09261/) (2026)
- [iMiGUE-3K: A Large-Scale Benchmark for Micro-Gesture Analysis with Self-Supervised Learning](https://mlsanity.com/paper/2605.17179/) (2026)
- [Rethinking the Role of Feature Engineering and Learning Strategies in Few-Shot Hidden Emotion Recognition](https://mlsanity.com/paper/2606.31249/) (2026)
- [Text-guided Fine-Grained Video Anomaly Understanding](https://mlsanity.com/paper/2511.00524/) (2025)
- [MAC 2026: Advancing Micro-Action Analysis Towards Fine-Grained Understanding](https://mlsanity.com/paper/2607.16284/) (2026)
- [Micro-DualNet: Dual-Path Spatio-Temporal Network for Micro-Action Recognition](https://mlsanity.com/paper/2604.21011/) (2026)
- [Motion Reinforces Appearance: RGB-Skeleton Gated Residual Fusion for Micro-Gesture Online Recognition](https://mlsanity.com/paper/2606.11645/) (2026)
- [A Multi-Modal Framework with Cross-Subject Pseudo-Labeling and Semantic Alignment for Micro-Gesture Recognition](https://mlsanity.com/paper/2606.13030/) (2026)
- [Zero-MELO: Test-Time Evidence Calibration with Multimodal LLMs for Zero-Shot Micro-Gesture Recognition](https://mlsanity.com/paper/2608.14854/) (2026)
- [Recognition-Conditioned Reasoning: A Training-Free Multimodal-LLM Pipeline for Fine-Grained Micro-Action Understanding](https://mlsanity.com/paper/2608.21022/) (2026)
- [SoftRerank: Hierarchical Soft Fusion with Candidate-Label Reranking for Long-Tailed Micro-Action Recognition](https://mlsanity.com/paper/2609.08221/) (2026)

## Abstract (arXiv)

In this paper, we present MM-Gesture, the solution developed by our team HFUT-VUT, which ranked 1st in the micro-gesture classification track of the 3rd MiGA Challenge at IJCAI 2025, achieving superior performance compared to previous state-of-the-art methods. MM-Gesture is a multimodal fusion framework designed specifically for recognizing subtle and short-duration micro-gestures (MGs), integrating complementary cues from joint, limb, RGB video, Taylor-series video, optical-flow video, and depth video modalities. Utilizing PoseConv3D and Video Swin Transformer architectures with a novel modality-weighted ensemble strategy, our method further enhances RGB modality performance through transfer learning pre-trained on the larger MA-52 dataset. Extensive experiments on the iMiGUE benchmark, including ablation studies across different modalities, validate the effectiveness of our proposed approach, achieving a top-1 accuracy of 73.213%. Code is available at: https://github.com/momiji-bit/MM-Gesture.