# KTPFormer: Kinematics and Trajectory Prior Knowledge-Enhanced Transformer for 3D Human Pose Estimation

arXiv: https://arxiv.org/abs/2404.00658 · HTML: https://mlsanity.com/paper/2404.00658/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [3D Human Pose Estimation with Spatial and Temporal Transformers](https://mlsanity.com/paper/2103.10455/) (2021)
- [Semantic Graph Convolutional Networks for 3D Human Pose Regression](https://mlsanity.com/paper/1904.03345/) (2019)
- [MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video](https://mlsanity.com/paper/2203.00859/) (2022)
- [Adaptive Graph Convolutional Neural Networks](https://mlsanity.com/paper/1801.03226/) (2018)
- [MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2111.12707/) (2021)
- [Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/1907.11346/) (2019)
- [Ordinal Depth Supervision for 3D Human Pose Estimation](https://mlsanity.com/paper/1805.04095/) (2018)
- [DiffPose: Toward More Reliable 3D Pose Estimation](https://mlsanity.com/paper/2211.16940/) (2022)
- [Graph Stacked Hourglass Networks for 3D Human Pose Estimation](https://mlsanity.com/paper/2103.16385/) (2021)
- [Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2103.14304/) (2021)
- [PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation](https://mlsanity.com/paper/2303.17472/) (2023)
- [Exploiting temporal information for 3D pose estimation](https://mlsanity.com/paper/1711.08585/) (2017)
- [Motion Guided 3D Pose Estimation from Videos](https://mlsanity.com/paper/2004.13985/) (2020)
- [Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows](https://mlsanity.com/paper/2107.13788/) (2021)
- [Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation](https://mlsanity.com/paper/2303.11579/) (2023)
- [P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation](https://mlsanity.com/paper/2203.07628/) (2022)
- [Conditional Directed Graph Convolution for 3D Human Pose Estimation](https://mlsanity.com/paper/2107.07797/) (2021)
- [GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Video](https://mlsanity.com/paper/2307.05853/) (2023)
- [3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information](https://mlsanity.com/paper/1608.03075/) (2016)
- [Direct Prediction of 3D Body Poses from Motion Compensated Sequences](https://mlsanity.com/paper/1511.06692/) (2015)
- [Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos](https://mlsanity.com/paper/2012.11806/) (2020)
- [Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation](https://mlsanity.com/paper/2302.07408/) (2023)

## Built on by (most cited first, 30 of 34)

- [AthletePose3D: A Benchmark Dataset for 3D Human Pose Estimation and Kinematic Validation in Athletic Movements](https://mlsanity.com/paper/2503.07499/) (2025)
- [Towards Balanced Multi-Modal Learning in 3D Human Pose Estimation](https://mlsanity.com/paper/2501.05264/) (2025)
- [Optimal-state Dynamics Estimation for Physics-based Human Motion Capture from Videos](https://mlsanity.com/paper/2410.07795/) (2024)
- [H2OT: Hierarchical Hourglass Tokenizer for Efficient Video Pose Transformers](https://mlsanity.com/paper/2509.06956/) (2025)
- [Optimizing Local-Global Dependencies for Accurate 3D Human Pose Estimation](https://mlsanity.com/paper/2412.19676/) (2024)
- [A Structure-aware and Motion-adaptive Framework for 3D Human Pose Estimation with Mamba](https://mlsanity.com/paper/2507.19852/) (2025)
- [QuaMo: Quaternion Motions for Vision-based 3D Human Kinematics Capture](https://mlsanity.com/paper/2601.19580/) (2026)
- [BLAPose: Enhancing 3D Human Pose Estimation with Bone Length Adjustment](https://mlsanity.com/paper/2410.20731/) (2024)
- [NanoHTNet: Nano Human Topology Network for Efficient 3D Human Pose Estimation](https://mlsanity.com/paper/2501.15763/) (2025)
- [SCJD: Sparse Correlation and Joint Distillation for Efficient 3D Human Pose Estimation](https://mlsanity.com/paper/2503.14097/) (2025)
- [PoseBench3D: A Cross-Dataset Analysis Framework for 3D Human Pose Estimation via Pose Lifting Networks](https://mlsanity.com/paper/2505.10888/) (2025)
- [Mamba-Driven Topology Fusion for Monocular 3D Human Pose Estimation](https://mlsanity.com/paper/2505.20611/) (2025)
- [DreamPose3D: Hallucinative Diffusion with Prompt Learning for 3D Human Pose Estimation](https://mlsanity.com/paper/2511.09502/) (2025)
- [FastDDHPose: Towards Unified, Efficient, and Disentangled 3D Human Pose Estimation](https://mlsanity.com/paper/2512.14162/) (2025)
- [Motion-Adaptive Multi-Scale Temporal Modelling with Skeleton-Constrained Spatial Graphs for Efficient 3D Human Pose Estimation](https://mlsanity.com/paper/2604.03652/) (2026)
- [MuPPet: Multi-person 2D-to-3D Pose Lifting](https://mlsanity.com/paper/2604.09715/) (2026)
- [Spectral Compression Transformer with Line Pose Graph for Monocular 3D Human Pose Estimation](https://mlsanity.com/paper/2505.21309/) (2025)
- [Physics-informed Ground Reaction Dynamics from Human Motion Capture](https://mlsanity.com/paper/2507.01340/) (2025)
- [StarPose: 3D Human Pose Estimation via Spatial-Temporal Autoregressive Diffusion](https://mlsanity.com/paper/2508.02056/) (2025)
- [SasMamba: A Lightweight Structure-Aware Stride State Space Model for 3D Human Pose Estimation](https://mlsanity.com/paper/2511.08872/) (2025)
- [LangPose: Language-Aligned Motion for Robust 3D Human Pose Estimation](https://mlsanity.com/paper/2409.00449/) (2024)
- [HyperDiff: Hypergraph Guided Diffusion Model for 3D Human Pose Estimation](https://mlsanity.com/paper/2508.14431/) (2025)
- [Efficient Diffusion-Based 3D Human Pose Estimation with Hierarchical Temporal Pruning](https://mlsanity.com/paper/2508.21363/) (2025)
- [PRGCN: A Graph Memory Network for Cross-Sequence Pattern Reuse in 3D Human Pose Estimation](https://mlsanity.com/paper/2510.19475/) (2025)
- [PoseMoE: Mixture-of-Experts Network for Monocular 3D Human Pose Estimation](https://mlsanity.com/paper/2512.16494/) (2025)
- [EgoPoseVR: Spatiotemporal Multi-Modal Reasoning for Egocentric Full-Body Pose in Virtual Reality](https://mlsanity.com/paper/2602.05590/) (2026)
- [SEAL-pose: Enhancing 3D Human Pose Estimation via a Learned Loss for Structural Consistency](https://mlsanity.com/paper/2602.20051/) (2026)
- [L2A: Learning to Accumulate Pose History for Accurate 3D Human Pose Estimation](https://mlsanity.com/paper/2605.08806/) (2026)
- [HYPERPOSE: Hyperbolic Kinematic Phase-Space Attention for 3D Human Pose Estimation](https://mlsanity.com/paper/2605.10100/) (2026)
- [Enhancing Domain Generalization in 3D Human Pose Estimation through Controllable Generative Augmentation](https://mlsanity.com/paper/2605.12198/) (2026)

## Abstract (arXiv)

This paper presents a novel Kinematics and Trajectory Prior Knowledge-Enhanced Transformer (KTPFormer), which overcomes the weakness in existing transformer-based methods for 3D human pose estimation that the derivation of Q, K, V vectors in their self-attention mechanisms are all based on simple linear mapping. We propose two prior attention modules, namely Kinematics Prior Attention (KPA) and Trajectory Prior Attention (TPA) to take advantage of the known anatomical structure of the human body and motion trajectory information, to facilitate effective learning of global dependencies and features in the multi-head self-attention. KPA models kinematic relationships in the human body by constructing a topology of kinematics, while TPA builds a trajectory topology to learn the information of joint motion trajectory across frames. Yielding Q, K, V vectors with prior knowledge, the two modules enable KTPFormer to model both spatial and temporal correlations simultaneously. Extensive experiments on three benchmarks (Human3.6M, MPI-INF-3DHP and HumanEva) show that KTPFormer achieves superior performance in comparison to state-of-the-art methods. More importantly, our KPA and TPA modules have lightweight plug-and-play designs and can be integrated into various transformer-based networks (i.e., diffusion-based) to improve the performance with only a very small increase in the computational overhead. The code is available at: https://github.com/JihuaPeng/KTPFormer.