# DragPoser: Motion Reconstruction from Variable Sparse Tracking Signals via Latent Space Optimization

arXiv: https://arxiv.org/abs/2406.14567 · HTML: https://mlsanity.com/paper/2406.14567/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 72 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 30 of 40)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [HuMoR: 3D Human Motion Model for Robust Pose Estimation](https://mlsanity.com/paper/2105.04668/) (2021)
- [Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold](https://mlsanity.com/paper/2305.10973/) (2023)
- [Character Controllers Using Motion VAEs](https://mlsanity.com/paper/2103.14274/) (2021)
- [Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors](https://mlsanity.com/paper/2203.08528/) (2022)
- [Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors](https://mlsanity.com/paper/2103.17265/) (2021)
- [Skeleton-Aware Networks for Deep Motion Retargeting](https://mlsanity.com/paper/2005.05732/) (2020)
- [Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs](https://mlsanity.com/paper/1703.08014/) (2017)
- [QuaterNet: A Quaternion-based Recurrent Model for Human Motion](https://mlsanity.com/paper/1805.06485/) (2018)
- [Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model](https://mlsanity.com/paper/2304.08577/) (2023)
- [Ego-Pose Estimation and Forecasting as Real-Time PD Control](https://mlsanity.com/paper/1906.03173/) (2019)
- [Learning Smooth Neural Functions via Lipschitz Regularization](https://mlsanity.com/paper/2202.08345/) (2022)
- [xR-EgoPose: Egocentric 3D Human Pose from an HMD Camera](https://mlsanity.com/paper/1907.10045/) (2019)
- [QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars](https://mlsanity.com/paper/2209.09391/) (2022)
- [Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation](https://mlsanity.com/paper/2203.15720/) (2022)
- [Mo2Cap2: Real-time Mobile 3D Motion Capture with a Cap-mounted Fisheye Camera](https://mlsanity.com/paper/1803.05959/) (2018)
- [Estimating Egocentric 3D Human Pose in Global Space](https://mlsanity.com/paper/2104.13454/) (2021)
- [FLAG: Flow-based 3D Avatar Generation from Sparse Observations](https://mlsanity.com/paper/2203.05789/) (2022)
- [SelfPose: 3D Egocentric Pose Estimation from a Headset Mounted Camera](https://mlsanity.com/paper/2011.01519/) (2020)
- [EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors](https://mlsanity.com/paper/2305.01599/) (2023)
- [Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time](https://mlsanity.com/paper/1810.04703/) (2018)
- [Modeling Human Motion with Quaternion-based Neural Networks](https://mlsanity.com/paper/1901.07677/) (2019)
- [LoBSTr: Real-time Lower-body Pose Prediction from Sparse Upper-body Tracking Signals](https://mlsanity.com/paper/2103.01500/) (2021)
- [BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis](https://mlsanity.com/paper/2304.11118/) (2023)
- [Realistic Full-Body Tracking from Sparse Observations via Joint-Level Modeling](https://mlsanity.com/paper/2308.08855/) (2023)
- [SOMA: Solving Optical Marker-Based MoCap Automatically](https://mlsanity.com/paper/2110.04431/) (2021)
- [Mocap Everyone Everywhere: Lightweight Motion Capture With Smartwatches and a Head-Mounted Camera](https://mlsanity.com/paper/2401.00847/) (2024)
- [HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media](https://mlsanity.com/paper/2110.07235/) (2021)
- [QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors](https://mlsanity.com/paper/2306.05666/) (2023)

## Built on by (most cited first, 3 of 3)

- [AnyTop: Character Animation Diffusion with Any Topology](https://mlsanity.com/paper/2502.17327/) (2025)
- [EnvPoser: Environment-aware Realistic Human Motion Estimation from Sparse Observations with Uncertainty Modeling](https://mlsanity.com/paper/2412.10235/) (2024)
- [Step2Motion: Locomotion Reconstruction from Pressure Sensing Insoles](https://mlsanity.com/paper/2510.22712/) (2025)

## Abstract (arXiv)

High-quality motion reconstruction that follows the user's movements can be achieved by high-end mocap systems with many sensors. However, obtaining such animation quality with fewer input devices is gaining popularity as it brings mocap closer to the general public. The main challenges include the loss of end-effector accuracy in learning-based approaches, or the lack of naturalness and smoothness in IK-based solutions. In addition, such systems are often finely tuned to a specific number of trackers and are highly sensitive to missing data e.g., in scenarios where a sensor is occluded or malfunctions. In response to these challenges, we introduce DragPoser, a novel deep-learning-based motion reconstruction system that accurately represents hard and dynamic on-the-fly constraints, attaining real-time high end-effectors position accuracy. This is achieved through a pose optimization process within a structured latent space. Our system requires only one-time training on a large human motion dataset, and then constraints can be dynamically defined as losses, while the pose is iteratively refined by computing the gradients of these losses within the latent space. To further enhance our approach, we incorporate a Temporal Predictor network, which employs a Transformer architecture to directly encode temporality within the latent space. This network ensures the pose optimization is confined to the manifold of valid poses and also leverages past pose data to predict temporally coherent poses. Results demonstrate that DragPoser surpasses both IK-based and the latest data-driven methods in achieving precise end-effector positioning, while it produces natural poses and temporally coherent motion. In addition, our system showcases robustness against on-the-fly constraint modifications, and exhibits exceptional adaptability to various input configurations and changes.