# BioPose: Biomechanically-accurate 3D Pose Estimation from Monocular Videos

arXiv: https://arxiv.org/abs/2501.07800 · HTML: https://mlsanity.com/paper/2501.07800/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 44 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 26 of 26)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [Humans in 4D: Reconstructing and Tracking Humans with Transformers](https://mlsanity.com/paper/2305.20091/) (2023)
- [PARE: Part Attention Regressor for 3D Human Body Estimation](https://mlsanity.com/paper/2104.08527/) (2021)
- [Learning 3D Human Dynamics from Video](https://mlsanity.com/paper/1812.01601/) (2018)
- [CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation](https://mlsanity.com/paper/2208.00571/) (2022)
- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video](https://mlsanity.com/paper/2011.08627/) (2020)
- [One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer](https://mlsanity.com/paper/2303.16160/) (2023)
- [EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild](https://mlsanity.com/paper/2308.16894/) (2023)
- [Human Motion Generation: A Survey](https://mlsanity.com/paper/2307.10894/) (2023)
- [TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representation](https://mlsanity.com/paper/2404.16752/) (2024)
- [Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformers](https://mlsanity.com/paper/2207.13820/) (2022)
- [Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes](https://mlsanity.com/paper/2104.07300/) (2021)
- [MoVi: A Large Multipurpose Motion and Video Dataset](https://mlsanity.com/paper/2003.01888/) (2020)
- [3D Human Mesh Estimation from Virtual Markers](https://mlsanity.com/paper/2303.11726/) (2023)
- [VQ-HPS: Human Pose and Shape Estimation in a Vector-Quantized Latent Space](https://mlsanity.com/paper/2312.08291/) (2023)
- [VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching](https://mlsanity.com/paper/2409.10756/) (2024)
- [Towards Single Camera Human 3D-Kinematics](https://mlsanity.com/paper/2301.05435/) (2023)
- [OpenCapBench: A Benchmark to Bridge Pose Estimation and Biomechanics](https://mlsanity.com/paper/2406.09788/) (2024)
- [A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities](https://mlsanity.com/paper/2407.07966/) (2024)

## Built on by (most cited first, 16 of 16)

- [Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone](https://mlsanity.com/paper/2507.08268/) (2025)
- [Monocular Biomechanical Tracking of Fingers with Inverse Kinematics to Foundation Models](https://mlsanity.com/paper/2605.09258/) (2026)
- [Multi-person Physics-based Pose Estimation for Combat Sports](https://mlsanity.com/paper/2504.08175/) (2025)
- [Grounding Intelligence in Movement](https://mlsanity.com/paper/2507.02771/) (2025)
- [MonoMSK: Monocular 3D Musculoskeletal Dynamics Estimation](https://mlsanity.com/paper/2511.19326/) (2025)
- [Age-Inclusive 3D Human Mesh Recovery for Action-Preserving Data Anonymization](https://mlsanity.com/paper/2512.05259/) (2025)
- [Monocular Markerless Motion Capture Enables Quantitative Assessment of Upper Extremity Reachable Workspace](https://mlsanity.com/paper/2602.13176/) (2026)
- [SAM4Dcap: Training-free Biomechanical Twin System from Monocular Video](https://mlsanity.com/paper/2602.13760/) (2026)
- [Biomechanical 3D Body: Self-Supervised Distillation of Biomechanical Pose from a 3D Body Foundation Model](https://mlsanity.com/paper/2608.29928/) (2026)
- [VOccl3D: A Video Benchmark Dataset for 3D Human Pose and Shape Estimation under real Occlusions](https://mlsanity.com/paper/2508.06757/) (2025)
- [Learning Quantised Structure-Preserving Motion Representations for Dance Fingerprinting](https://mlsanity.com/paper/2604.00927/) (2026)
- [AIGaitor: Privacy-preserving and cloud-free motion analysis for everyone, using edge computing](https://mlsanity.com/paper/2605.21421/) (2026)
- [Again-Pose: Anchor-Guided Adaptive Inter-Frame Motion Cues Propagating for High-quality Human Pose Reconstruction](https://mlsanity.com/paper/2606.29230/) (2026)
- [Pose-to-Biomechanics: Bridging 3D Human Pose Estimation and Biomechanical Attribute Prediction](https://mlsanity.com/paper/2607.08725/) (2026)
- [Direct Clinical Joint Angle Extraction from Parametric Body Model Rotation Matrices](https://mlsanity.com/paper/2607.17639/) (2026)
- [MuyBridge: Mobile Human Center-of-Mass Estimation from Monocular Video via Sparse Fusion](https://mlsanity.com/paper/2609.02854/) (2026)

## Abstract (arXiv)

Recent advancements in 3D human pose estimation from single-camera images and videos have relied on parametric models, like SMPL. However, these models oversimplify anatomical structures, limiting their accuracy in capturing true joint locations and movements, which reduces their applicability in biomechanics, healthcare, and robotics. Biomechanically accurate pose estimation, on the other hand, typically requires costly marker-based motion capture systems and optimization techniques in specialized labs. To bridge this gap, we propose BioPose, a novel learning-based framework for predicting biomechanically accurate 3D human pose directly from monocular videos. BioPose includes three key components: a Multi-Query Human Mesh Recovery model (MQ-HMR), a Neural Inverse Kinematics (NeurIK) model, and a 2D-informed pose refinement technique. MQ-HMR leverages a multi-query deformable transformer to extract multi-scale fine-grained image features, enabling precise human mesh recovery. NeurIK treats the mesh vertices as virtual markers, applying a spatial-temporal network to regress biomechanically accurate 3D poses under anatomical constraints. To further improve 3D pose estimations, a 2D-informed refinement step optimizes the query tokens during inference by aligning the 3D structure with 2D pose observations. Experiments on benchmark datasets demonstrate that BioPose significantly outperforms state-of-the-art methods. Project website: urlhttps://m-usamasaleem.github.io/publication/BioPose/BioPose.html.