# H2R-Bench: Benchmarking Human-to-Robot Manipulation Video Generation in World Models arXiv: https://arxiv.org/abs/2608.13049 · HTML: https://mlsanity.com/paper/2608.13049/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in Embodied and robot world models, one of 11 directions in World models. - Builds on 32 of its 69 references that are in the map. - First posted on arXiv 13 Aug 2026. ## Placed in - World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/) ## Builds on (most cited first, 30 of 32) - [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025) - [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024) - [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022) - [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024) - [R3M: A Universal Visual Representation for Robot Manipulation](https://mlsanity.com/paper/2203.12601/) (2022) - [VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training](https://mlsanity.com/paper/2210.00030/) (2022) - [LTX-2: Efficient Joint Audio-Visual Foundation Model](https://mlsanity.com/paper/2601.03233/) (2026) - [EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video](https://mlsanity.com/paper/2505.11709/) (2025) - [HunyuanVideo 1.5 Technical Report](https://mlsanity.com/paper/2511.18870/) (2025) - [Seedance 2.0: Advancing Video Generation for World Complexity](https://mlsanity.com/paper/2604.14148/) (2026) - [AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos](https://mlsanity.com/paper/1912.04443/) (2019) - [EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data](https://mlsanity.com/paper/2602.16710/) (2026) - [LongCat-Video Technical Report](https://mlsanity.com/paper/2510.22200/) (2025) - [Kling-Omni Technical Report](https://mlsanity.com/paper/2512.16776/) (2025) - [Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos](https://mlsanity.com/paper/2510.21571/) (2025) - [Masquerade: Learning from In-the-wild Human Videos using Data-Editing](https://mlsanity.com/paper/2508.09976/) (2025) - [Rethinking Video Generation Model for the Embodied World](https://mlsanity.com/paper/2601.15282/) (2026) - [MimicDreamer: Aligning Human and Robot Demonstrations for Scalable VLA Training](https://mlsanity.com/paper/2509.22199/) (2025) - [H2R: A Human-to-Robot Data Augmentation for Robot Pre-training from Videos](https://mlsanity.com/paper/2505.11920/) (2025) - [SkyReels-V3 Technique Report](https://mlsanity.com/paper/2601.17323/) (2026) - [Mitty: Diffusion-based Human-to-Robot Video Generation](https://mlsanity.com/paper/2512.17253/) (2025) - [DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing](https://mlsanity.com/paper/2602.12205/) (2026) - [Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation](https://mlsanity.com/paper/2606.17030/) (2026) - [UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing](https://mlsanity.com/paper/2602.02437/) (2026) - [H2R-Grounder: A Paired-Data-Free Paradigm for Translating Human Interaction Videos into Physically Grounded Robot Videos](https://mlsanity.com/paper/2512.09406/) (2025) - [RoboTrustBench: Benchmarking the Trustworthiness of Video World Models for Robotic Manipulation](https://mlsanity.com/paper/2606.01600/) (2026) - [Robot Learning from Human Videos: A Survey](https://mlsanity.com/paper/2604.27621/) (2026) - [Rein3D: Reinforced 3D Indoor Scene Generation with Panoramic Video Diffusion Models](https://mlsanity.com/paper/2604.10578/) (2026) - [R3DP: Real-Time 3D-Aware Policy for Embodied Manipulation](https://mlsanity.com/paper/2603.14498/) (2026) - [Reason, Then Re-reason: Cross-view Revisiting Improves Spatial Reasoning](https://mlsanity.com/paper/2606.11683/) (2026) ## Built on by (most cited first, 4 of 4) - [In-Context Learning for Robots: Methods and Applications](https://mlsanity.com/paper/2609.36012/) (2026) - [TRACE-Bench: Decomposing and Diagnosing Multi-Reference Image Generation](https://mlsanity.com/paper/2608.16765/) (2026) - [HappyWorld-Bench](https://mlsanity.com/paper/2609.24308/) (2026) - [Ego2Act: Evaluating Goal-Directed Manipulation in Egocentric Video Generation](https://mlsanity.com/paper/2610.01092/) (2026) ## Abstract (arXiv) Large-scale manipulation data is essential for robot learning, yet collecting robot demonstrations remains expensive and difficult to scale. Meanwhile, abundant egocentric human manipulation videos provide rich behavioral experiences, but transferring them across embodiments remains challenging due to differences between human hands and robotic end-effectors. Recent advances in video world models offer a promising pathway to synthesize robot-centric manipulation videos from human observations, while their cross-embodiment transfer capability remains largely unexplored. Therefore, we introduce H2R-Bench, a benchmark for evaluating cross-embodiment human-to-robot manipulation video generation, where models transform egocentric human demonstrations into robot manipulation videos under specified embodiments. Each benchmark instance contains a human demonstration video, target embodiment constraints, and source-grounded annotations covering task goals, action events, functional contacts, and object responses. H2R-Bench evaluates generated videos through five dimensions, including goal-state completion, action-event completion, functional contact transfer, embodiment correctness, and general video quality. We benchmark eleven state-of-the-art video generation models across six manipulation families and two robot embodiments. Our evaluation reveals that current video world models remain limited in human-to-robot manipulation transfer: even leading models often fail in embodiment consistency, functional interaction, and task execution. H2R-Bench provides a systematic diagnostic framework for evaluating whether video world models can bridge the human-to-robot embodiment gap and convert human manipulation observations into robot-centric training resources.