# Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning

arXiv: https://arxiv.org/abs/2308.03975 · HTML: https://mlsanity.com/paper/2308.03975/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 54 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 30 of 41)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining](https://mlsanity.com/paper/2302.02318/) (2023)
- [An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data](https://mlsanity.com/paper/1611.06067/) (2016)
- [A New Representation of Skeleton Sequences for 3D Action Recognition](https://mlsanity.com/paper/1703.03492/) (2017)
- [3D Human Action Representation Learning via Cross-View Consistency Pursuit](https://mlsanity.com/paper/2104.14466/) (2021)
- [Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks](https://mlsanity.com/paper/2008.07404/) (2020)
- [Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation](https://mlsanity.com/paper/2212.04145/) (2022)
- [MS2L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition](https://mlsanity.com/paper/2010.05599/) (2020)
- [PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition](https://mlsanity.com/paper/1911.12409/) (2019)
- [Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition](https://mlsanity.com/paper/2112.03590/) (2021)
- [Skeleton-Contrastive 3D Action Representation Learning](https://mlsanity.com/paper/2108.03656/) (2021)
- [Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/2003.05438/) (2020)
- [MixCo: Mix-up Contrastive Learning for Visual Representation](https://mlsanity.com/paper/2010.06300/) (2020)
- [HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations](https://mlsanity.com/paper/2303.06242/) (2023)
- [CO2: Consistent Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/2010.02217/) (2020)
- [CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation](https://mlsanity.com/paper/2208.12448/) (2022)
- [Contrastive Positive Mining for Unsupervised 3D Action Representation Learning](https://mlsanity.com/paper/2208.03497/) (2022)
- [Global-local Motion Transformer for Unsupervised Skeleton-based Action Learning](https://mlsanity.com/paper/2207.06101/) (2022)
- [Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations](https://mlsanity.com/paper/2211.13466/) (2022)

## Built on by (most cited first, 19 of 19)

- [Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition](https://mlsanity.com/paper/2411.18941/) (2024)
- [USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation](https://mlsanity.com/paper/2412.09220/) (2024)
- [MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion](https://mlsanity.com/paper/2409.10473/) (2024)
- [Heterogeneous Skeleton-Based Action Representation Learning](https://mlsanity.com/paper/2506.03481/) (2025)
- [Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learning](https://mlsanity.com/paper/2405.20606/) (2024)
- [Foundation Model for Skeleton-Based Human Action Understanding](https://mlsanity.com/paper/2508.12586/) (2025)
- [Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2410.20349/) (2024)
- [3D Skeleton-Based Action Recognition: A Review](https://mlsanity.com/paper/2506.00915/) (2025)
- [Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond](https://mlsanity.com/paper/2406.02978/) (2024)
- [STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences](https://mlsanity.com/paper/2407.10935/) (2024)
- [Towards Efficient General Feature Prediction in Masked Skeleton Modeling](https://mlsanity.com/paper/2509.03609/) (2025)
- [DuoCLR: Dual-Surrogate Contrastive Learning for Skeleton-based Human Action Segmentation](https://mlsanity.com/paper/2509.05543/) (2025)
- [Spatial Hierarchy and Temporal Attention Guided Cross Masking for Self-supervised Skeleton-based Action Recognition](https://mlsanity.com/paper/2409.17951/) (2024)
- [MaskSem: Semantic-Guided Masking for Learning 3D Hybrid High-Order Motion Representation](https://mlsanity.com/paper/2508.12948/) (2025)
- [Skeleton-Snippet Contrastive Learning with Multiscale Feature Fusion for Action Localization](https://mlsanity.com/paper/2512.16504/) (2025)
- [Affinity Contrastive Learning for Skeleton-based Human Activity Understanding](https://mlsanity.com/paper/2601.16694/) (2026)
- [Skeleton-to-Image Encoding: Enabling Skeleton Representation Learning via Vision-Pretrained Models](https://mlsanity.com/paper/2603.05963/) (2026)
- [Beyond Binary Contrast: Modeling Continuous Skeleton Action Spaces with Transitional Anchors](https://mlsanity.com/paper/2604.17914/) (2026)
- [Action Motifs: Self-Supervised Hierarchical Representation of Human Body Movements](https://mlsanity.com/paper/2604.28173/) (2026)

## Abstract (arXiv)

Self-supervised learning has proved effective for skeleton-based human action understanding, which is an important yet challenging topic. Previous works mainly rely on contrastive learning or masked motion modeling paradigm to model the skeleton relations. However, the sequence-level and joint-level representation learning cannot be effectively and simultaneously handled by these methods. As a result, the learned representations fail to generalize to different downstream tasks. Moreover, combining these two paradigms in a naive manner leaves the synergy between them untapped and can lead to interference in training. To address these problems, we propose Prompted Contrast with Masked Motion Modeling, PCMrm 3, for versatile 3D action representation learning. Our method integrates the contrastive learning and masked prediction tasks in a mutually beneficial manner, which substantially boosts the generalization capacity for various downstream tasks. Specifically, masked prediction provides novel training views for contrastive learning, which in turn guides the masked prediction training with high-level semantic information. Moreover, we propose a dual-prompted multi-task pretraining strategy, which further improves model representations by reducing the interference caused by learning the two different pretext tasks. Extensive experiments on five downstream tasks under three large-scale datasets are conducted, demonstrating the superior generalization capacity of PCMrm 3 compared to the state-of-the-art works. Our project is publicly available at: https://jhang2020.github.io/Projects/PCM3/PCM3.html .