# FRAME: Floor-aligned Representation for Avatar Motion from Egocentric Video

arXiv: https://arxiv.org/abs/2503.23094 · HTML: https://mlsanity.com/paper/2503.23094/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 53 references that are in the map.
- First posted on arXiv 29 Mar 2025.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://mlsanity.com/paper/2311.18259/) (2023)
- [Humans in 4D: Reconstructing and Tracking Humans with Transformers](https://mlsanity.com/paper/2305.20091/) (2023)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors](https://mlsanity.com/paper/2203.08528/) (2022)
- [Hand Pose Estimation via Latent 2.5D Heatmap Regression](https://mlsanity.com/paper/1804.09534/) (2018)
- [AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing](https://mlsanity.com/paper/2207.13784/) (2022)
- [Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model](https://mlsanity.com/paper/2304.08577/) (2023)
- [Ego-Body Pose Estimation via Ego-Head Pose Estimation](https://mlsanity.com/paper/2212.04636/) (2022)
- [Ego-Pose Estimation and Forecasting as Real-Time PD Control](https://mlsanity.com/paper/1906.03173/) (2019)
- [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)
- [UnrealEgo: A New Dataset for Robust Egocentric 3D Human Motion Capture](https://mlsanity.com/paper/2208.01633/) (2022)
- [Estimating Egocentric 3D Human Pose in Global Space](https://mlsanity.com/paper/2104.13454/) (2021)
- [IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds](https://mlsanity.com/paper/2304.12518/) (2023)
- [Human Pose Regression by Combining Indirect Part Detection and Contextual Information](https://mlsanity.com/paper/1710.02322/) (2017)
- [EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors](https://mlsanity.com/paper/2305.01599/) (2023)
- [Estimating Egocentric 3D Human Pose in the Wild with External Weak Supervision](https://mlsanity.com/paper/2201.07929/) (2022)
- [Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors](https://mlsanity.com/paper/2312.02196/) (2023)
- [HMD^2: Environment-aware Motion Generation from Single Egocentric Head-Mounted Device](https://mlsanity.com/paper/2409.13426/) (2024)
- [3D Human Pose Perception from Egocentric Stereo Videos](https://mlsanity.com/paper/2401.00889/) (2023)
- [Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views](https://mlsanity.com/paper/2309.11962/) (2023)
- [HMD-NeMo: Online 3D Avatar Motion Generation From Sparse Observations](https://mlsanity.com/paper/2308.11261/) (2023)
- [Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting](https://mlsanity.com/paper/2402.18330/) (2024)
- [SimpleEgo: Predicting Probabilistic Body Pose from Egocentric Cameras](https://mlsanity.com/paper/2401.14785/) (2024)

## Built on by (most cited first, 5 of 5)

- [EgoPoseFormer v2: Accurate Egocentric Human Motion Estimation for AR/VR](https://mlsanity.com/paper/2603.04090/) (2026)
- [Egocentric Visibility-Aware Human Pose Estimation](https://mlsanity.com/paper/2602.23618/) (2026)
- [Towards Real-World Wearable Motion Reconstruction](https://mlsanity.com/paper/2607.09780/) (2026)
- [Human Motion Estimation with Everyday Wearables](https://mlsanity.com/paper/2512.21209/) (2025)
- [EgoRelight: Egocentric Human Capture and Illumination Recovery for Relightable and Photoreal Avatar Rendering](https://mlsanity.com/paper/2605.28401/) (2026)

## Abstract (arXiv)

Egocentric motion capture with a head-mounted body-facing stereo camera is crucial for VR and AR applications but presents significant challenges such as heavy occlusions and limited annotated real-world data. Existing methods rely on synthetic pretraining and struggle to generate smooth and accurate predictions in real-world settings, particularly for lower limbs. Our work addresses these limitations by introducing a lightweight VR-based data collection setup with on-board, real-time 6D pose tracking. Using this setup, we collected the most extensive real-world dataset for ego-facing ego-mounted cameras to date in size and motion variability. Effectively integrating this multimodal input -- device pose and camera feeds -- is challenging due to the differing characteristics of each data source. To address this, we propose FRAME, a simple yet effective architecture that combines device pose and camera feeds for state-of-the-art body pose prediction through geometrically sound multimodal integration and can run at 300 FPS on modern hardware. Lastly, we showcase a novel training strategy to enhance the model's generalization capabilities. Our approach exploits the problem's geometric properties, yielding high-quality motion capture free from common artifacts in prior works. Qualitative and quantitative evaluations, along with extensive comparisons, demonstrate the effectiveness of our method. Data, code, and CAD designs will be available at https://vcai.mpi-inf.mpg.de/projects/FRAME/