# RGB-D Video Generation for Improving Human-to-Robot Object Handover Prediction

arXiv: https://arxiv.org/abs/2608.13028 · HTML: https://mlsanity.com/paper/2608.13028/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 42 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Builds on (most cited first, 30 of 33)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Towards Accurate Generative Models of Video: A New Metric & Challenges](https://mlsanity.com/paper/1812.01717/) (2018)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model](https://mlsanity.com/paper/2311.16498/) (2023)
- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [Effective Whole-body Pose Estimation with Two-stages Distillation](https://mlsanity.com/paper/2307.15880/) (2023)
- [DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos](https://mlsanity.com/paper/2409.02095/) (2024)
- [MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance](https://mlsanity.com/paper/2406.19680/) (2024)
- [DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion](https://mlsanity.com/paper/2304.06025/) (2023)
- [MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion](https://mlsanity.com/paper/2311.12052/) (2023)
- [Animate-X: Universal Character Image Animation with Enhanced Motion Representation](https://mlsanity.com/paper/2410.10306/) (2024)
- [StableAnimator: High-Quality Identity-Preserving Human Image Animation](https://mlsanity.com/paper/2411.17697/) (2024)
- [Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance](https://mlsanity.com/paper/2502.06145/) (2025)
- [LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds](https://mlsanity.com/paper/2503.10625/) (2025)
- [Object Handovers: a Review for Robotics](https://mlsanity.com/paper/2007.12952/) (2020)
- [AnchorCrafter: Animate Cyber-Anchors Selling Your Products via Human-Object Interacting Video Generation](https://mlsanity.com/paper/2411.17383/) (2024)
- [Reactive Human-to-Robot Handovers of Arbitrary Objects](https://mlsanity.com/paper/2011.08961/) (2020)
- [MVGamba: Unify 3D Content Generation as State Space Sequence Modeling](https://mlsanity.com/paper/2406.06367/) (2024)
- [ManiVideo: Generating Hand-Object Manipulation Video with Dexterous and Generalizable Grasping](https://mlsanity.com/paper/2412.16212/) (2024)
- [H2O: A Benchmark for Visual Human-human Object Handover Analysis](https://mlsanity.com/paper/2104.11466/) (2021)
- [HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers](https://mlsanity.com/paper/2205.09747/) (2022)
- [GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation, Demonstration, and Imitation](https://mlsanity.com/paper/2401.00929/) (2024)
- [Mitty: Diffusion-based Human-to-Robot Video Generation](https://mlsanity.com/paper/2512.17253/) (2025)
- [SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers](https://mlsanity.com/paper/2311.05599/) (2023)
- [HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs](https://mlsanity.com/paper/2309.16524/) (2023)
- [Diffusion-Based Depth Inpainting for Transparent and Reflective Objects](https://mlsanity.com/paper/2410.08567/) (2024)
- [DexH2R: A Benchmark for Dynamic Dexterous Grasping in Human-to-Robot Handover](https://mlsanity.com/paper/2506.23152/) (2025)
- [HOH: Markerless Multimodal Human-Object-Human Handover Dataset with Large Object Count](https://mlsanity.com/paper/2310.00723/) (2023)

## Abstract (arXiv)

Human-to-robot (H2R) object handover is a fundamental capability for human-robot collaboration, yet progress is hindered by the scarcity of large-scale, human-centric datasets and the significant sim-to-real gap. To address these challenges, we introduce Hand2Bot, an RGB-D video dataset that provides rich contextual information such as body posture and facial expressions, specifically collected for handover scenarios with real-world noise patterns. We further propose PassGen, a generative pipeline that leverages stable video diffusion and an Intention-Aware Temporal Face Encoder to synthesize realistic handover sequences while ensuring hand-object consistency. To bridge the sim-to-real gap, we implement a morphology-based depth editing strategy that replicates realistic sensor noise found in physical depth maps. Experimental evaluations demonstrate that our framework achieves high intention identification accuracy and low false trigger rates in both ablation studies and real-world deployment on a physical robot platform. Our results confirm that training on PassGen allows for robust zero-shot transfer and earlier intention anticipation compared to traditional hand-centric baselines, effectively enabling socially aware robotic behavior in shared workspaces.