# Masquerade: Learning from In-the-wild Human Videos using Data-Editing

arXiv: https://arxiv.org/abs/2508.09976 · HTML: https://mlsanity.com/paper/2508.09976/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2025.

## Built on by (most cited first, 30 of 44)

- [Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models](https://mlsanity.com/paper/2606.17846/) (2026)
- [In-N-On: Scaling Egocentric Manipulation with in-the-wild and on-task Data](https://mlsanity.com/paper/2511.15704/) (2025)
- [A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation](https://mlsanity.com/paper/2602.01067/) (2026)
- [Dexterous World Models](https://mlsanity.com/paper/2512.17907/) (2025)
- [RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulation](https://mlsanity.com/paper/2604.19092/) (2026)
- [HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos](https://mlsanity.com/paper/2605.24934/) (2026)
- [Mitty: Diffusion-based Human-to-Robot Video Generation](https://mlsanity.com/paper/2512.17253/) (2025)
- [Egocentric World Model for Photorealistic Hand-Object Interaction Synthesis](https://mlsanity.com/paper/2603.13615/) (2026)
- [Developing Vision-Language-Action Model from Egocentric Videos](https://mlsanity.com/paper/2509.21986/) (2025)
- [OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning](https://mlsanity.com/paper/2512.13100/) (2025)
- [H2R-Grounder: A Paired-Data-Free Paradigm for Translating Human Interaction Videos into Physically Grounded Robot Videos](https://mlsanity.com/paper/2512.09406/) (2025)
- [Robot Learning from Human Videos: A Survey](https://mlsanity.com/paper/2604.27621/) (2026)
- [X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations](https://mlsanity.com/paper/2511.04671/) (2025)
- [Breaking Lock-In: Preserving Steerability under Low-Data VLA Post-Training](https://mlsanity.com/paper/2604.23121/) (2026)
- [AoE: Always-on Egocentric Human Video Collection for Embodied AI](https://mlsanity.com/paper/2602.23893/) (2026)
- [A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies](https://mlsanity.com/paper/2604.13645/) (2026)
- [Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling](https://mlsanity.com/paper/2604.28185/) (2026)
- [LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition](https://mlsanity.com/paper/2606.11628/) (2026)
- [Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA](https://mlsanity.com/paper/2606.22836/) (2026)
- [HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing](https://mlsanity.com/paper/2608.12122/) (2026)
- [OmniHumanoid: Streaming Cross-Embodiment Video Generation with Paired-Free Adaptation](https://mlsanity.com/paper/2605.12038/) (2026)
- [From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data](https://mlsanity.com/paper/2606.00054/) (2026)
- [H2R-Bench: Benchmarking Human-to-Robot Manipulation Video Generation in World Models](https://mlsanity.com/paper/2608.13049/) (2026)
- [Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data](https://mlsanity.com/paper/2606.08107/) (2026)
- [RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation](https://mlsanity.com/paper/2607.06558/) (2026)
- [SiMDex: Mining Similar Egocentric Videos for Cross-Embodiment Dexterous Manipulation](https://mlsanity.com/paper/2608.04196/) (2026)
- [TrajBooster: Boosting Humanoid Whole-Body Manipulation via Trajectory-Centric Learning](https://mlsanity.com/paper/2509.11839/) (2025)
- [EmbodiSwap for Zero-Shot Robot Imitation Learning](https://mlsanity.com/paper/2510.03706/) (2025)
- [Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos](https://mlsanity.com/paper/2602.13197/) (2026)
- [LIDEA: Human-to-Robot Imitation Learning via Implicit Feature Distillation and Explicit Geometry Alignment](https://mlsanity.com/paper/2604.10677/) (2026)

## Abstract (arXiv)

Robot manipulation research still suffers from significant data scarcity: even the largest robot datasets are orders of magnitude smaller and less diverse than those that fueled recent breakthroughs in language and vision. We introduce Masquerade, a method that edits in-the-wild egocentric human videos to bridge the visual embodiment gap between humans and robots and then learns a robot policy with these edited videos. Our pipeline turns each human video into robotized demonstrations by (i) estimating 3-D hand poses, (ii) inpainting the human arms, and (iii) overlaying a rendered bimanual robot that tracks the recovered end-effector trajectories. Pre-training a visual encoder to predict future 2-D robot keypoints on 675K frames of these edited clips, and continuing that auxiliary loss while fine-tuning a diffusion policy head on only 50 robot demonstrations per task, yields policies that generalize significantly better than prior work. On three long-horizon, bimanual kitchen tasks evaluated in three unseen scenes each, Masquerade outperforms baselines by 5-6x. Ablations show that both the robot overlay and co-training are indispensable, and performance scales logarithmically with the amount of edited human video. These results demonstrate that explicitly closing the visual embodiment gap unlocks a vast, readily available source of data from human videos that can be used to improve robot policies.