Source-linked AI summary
Mitty: Diffusion-based Human-to-Robot Video Generation
Yiren Song, Cheng Liu, Weijia Mao, Mike Zheng Shou
TL;DR
Robots learning from human videos could support cross-task and cross-environment generalization, but intermediate representations can lose detail and accumulate errors. Mitty uses a pretrained video diffusion model and synthesized human–robot pairs to generate robot-execution videos directly from demonstrations. It reports strong performance across Human2Robot and EPIC-Kitchens, while remaining short of a full closed-loop control pipeline.
Problem
Intermediate representations in human-to-robot video methods can lose demonstration information and accumulate errors, while aligned human–robot pairs are scarce.
Source: §1 · p. 1Method
Mitty uses in-context learning with a pretrained video diffusion model to generate robot-execution videos directly from demonstrations, and synthesizes paired training videos from egocentric data.
Source: §1 · p. 2Results
Takeaways & Limitations
High-fidelity, semantically aligned Human2Robot videos provide structured supervision for future video-to-policy inversion.
Source: §1 · p. 2, §5 · p. 8Mitty generates robot-arm videos but not control-ready action sequences, and has not been evaluated in full closed-loop real-robot settings.
Source: §5 · p. 8
Abstract
from arXiv · showhide
Learning directly from human demonstration videos is a key milestone toward scalable and generalizable robot learning. Yet existing methods rely on intermediate representations such as keypoints or trajectories, introducing information loss and cumulative errors that harm temporal and visual consistency. We present Mitty, a Diffusion Transformer that enables video In-Context Learning for end-to-end Human2Robot video generation. Built on a pretrained video diffusion model, Mitty leverages strong visual-temporal priors to translate human demonstrations into robot-execution videos without action labels or intermediate abstractions. Demonstration videos are compressed into condition tokens and fused with robot denoising tokens through bidirectional attention during diffusion. To mitigate paired-data scarcity, we also develop an automatic synthesis pipeline that produces high-quality human-robot pairs from large egocentric datasets. Experiments on Human2Robot and EPIC-Kitchens show that Mitty delivers state-of-the-art results, strong generalization to unseen environments, and new insights for scalable robot learning from human observations.
1. Introduction
Mitty directly translates human demonstration videos into robot-execution videos, addressing information loss and error accumulation in intermediate-representation pipelines. Its paired-data synthesis and in-context conditioning support strong reported performance and generalization.
Motivation
- Existing keypoint-, trajectory-, or depth-based pipelines can lose demonstration detail and accumulate estimation errors, while aligned human–robot video pairs remain scarce.The only public H2R dataset described contains 2,600 pairs across nine tasks.Source: §1 · pp. 1–2
Paired-data synthesis
- An automatic pipeline converts egocentric videos into human–robot pairs by estimating hand keypoints, removing hands, inpainting backgrounds, and rendering mapped robot poses.The authors say this synthesis strategy increases scale and fine-grained temporal consistency for training and generalization.Source: §1 · p. 2
Mitty
- Mitty uses in-context learning with a pretrained video diffusion model to generate robot-execution videos directly from human demonstrations, without explicit action labels.Human videos become clean condition tokens fused with robot denoising tokens through bidirectional attention; inference can be first-frame-controlled or zero-frame.Source: §1 · p. 2
Results
- Mitty significantly outperforms existing baselines across Human2Robot and EPIC-Kitchens, with strong generalization and advantages over rendering pipelines and video-editing systems.The authors also report the best task success rate and human preference, and the second-highest embodiment consistency.Source: §1 · p. 2
2. Related Works
The related work traces video generation advances, efforts to learn robot skills from human videos, and the use of in-context learning for adaptation. Mitty combines these directions to generate robot videos directly from human demonstrations.
2.1. Video Generation Models
- Diffusion Transformers follow GAN- and UNet-based video-generation approaches and support temporally coherent conditional generation, including controllable video and world-modeling applications.Prior work also applies pretrained video-generation models to robotics and mechanical manipulation [7].Source: §2.1 · p. 2
2.2. Learning from Human Videos
- Human-video learning can scale demonstrations beyond teleoperation, but many approaches still need robot data or specialized hardware, and cross-embodiment transfer remains difficult.Mitty instead generates robot videos directly from demonstrations without extracting pose, trajectories, or depth.Source: §2.2 · pp. 2–3
2.3. In-Context Learning
- Prior in-context learning spans visual generation and preliminary robotic visuomotor policies, while data-collection costs and limited task diversity constrain adaptation.Mitty applies in-context learning with Wan 2.2 to translate demonstrations into robot-arm executions while maintaining visual and action consistency.Source: §2.3 · p. 3
3. Method
Mitty is a unified video In-Context Learning framework that generates robot-execution videos directly from human demonstrations, with a synthetic paired-data pipeline supporting training.
3.1. Overall Architecture
- Mitty supports zero-frame Human2Robot generation and first-frame-conditioned generation, sharing parameters while allowing control over the robot’s initial state.The first mode generates from a human demonstration alone; the second also uses an initial robot frame.Source: §3.1 · p. 3
3.2. Video In-Context Learning via Bidirectional Attention
- Human video tokens remain clean while robot latents are progressively noised, enabling conditional generation without explicit action or trajectory labels.Bidirectional attention exchanges information between the human-condition and robot-denoising tokens at each layer.Source: §3.2 · p. 3
3.3. Dataset Construction
- An automated pipeline maps egocentric hand keypoints to robot poses, renders robot arms into inpainted videos, and filters the resulting paired clips.Human-in-the-loop filtering removes low-quality samples; the pipeline yields over 6,000 synthetic human–robot pairs.Source: §3.3 · p. 4, Figure 3 · p. 5
4. Experiments
Mitty is evaluated on Human2Robot and EPIC-Kitchens using video-quality, task-success, preference, and embodiment measures against in-context and video-editing baselines. Results favor Mitty on task success and human preference, while ablations highlight visual-reference conditioning and separate dataset training.
4.1. Setup.
- The evaluation uses Wan 2.2 TI2V models, with the 5B model trained for 20k steps and the 14B model for 10k steps; both use LoRA fine-tuning and 416×224 resolution.Training and inference use two H200 GPUs and an effective batch size of 4.Source: §4.1 · p. 5
4.2. Datasets ad Benchmark.
- Testing uses 11,788 Human2Robot clips with 500 held out and 34,820 EPIC-Kitchens clips with 200 held out, split evenly between seen and unseen scenes.Videos are resampled to 8 FPS and divided into 41-frame clips.Source: §4.2 · p. 5
4.3. Metric.
- Video quality is measured with FVD, PSNR, MSE, and SSIM, while experts assess task success and normalized CLIP, DreamSim, and human scores assess embodiment consistency.Human preference is measured through a survey with 20 valid responses.Source: §4.3 · p. 5
4.4. Baseline Methods.
- Baselines include Wan 2.2 in-context configurations and general video-editing methods, including Aleph, Kling, and MoCha.The comparison varies model scale, reference-video and task-description inputs, and separate versus mixed training.Source: §4.4 · p. 5
4.5. Results.
- Mitty achieves the best task success rate and human preference, plus the second-highest embodiment consistency, while first-frame conditioning improves video-generation metrics across both datasets.Table 3 compares task-level success rate, human preference, and embodiment consistency; Table 1 covers video metrics and first-frame conditioning.Source: Table 3 · p. 7, §4.5 · p. 7, Table 1 · p. 7
4.6. Ablation Study.
- The TI2V-5B model with first-frame conditioning is the default baseline; removing the human reference video degrades metrics more than removing task text, especially on EPIC-Kitchens, and separate training beats mixed training.The reference-video ablation is more severe on EPIC-Kitchens, whose scenes and viewpoints are more diverse.Source: §4.6 · p. 7, Table 2 · p. 7
4.7. Comparison with Masquerade’s Data Pipeline.
- Masquerade’s hand segmentation, keypoint estimation, inpainting, and rendering stages can compound errors, while human-in-the-loop filtering yields a curated paired dataset for robust end-to-end training.The comparison analyzes Masquerade’s data-synthesis pipeline, not its full system.Source: §4.7 · pp. 7–8, Figure 5 · p. 8
4.8. Compare with Video Editing Methods.
- Video-editing baselines condition on a single robot-arm image but still produce deformation and structural distortions, whereas Mitty preserves the arm’s appearance and structure.The comparison includes two commercial APIs and one open-source method.Source: §4.8 · p. 8, Figure 6 · p. 8
5. Limitations and Future Work
Mitty generates robot-execution videos but does not yet produce control-ready actions or undergo full closed-loop real-robot evaluation. Future work targets action or policy prediction, closed-loop testing, and more automated, physically grounded evaluation.
Limitations
- Mitty does not output control-ready action sequences and has not been evaluated in full closed-loop settings on real robots.Task success is assessed by experts reviewing generated videos rather than physical rollouts.Source: §5 · p. 8
Future Work
- The authors plan to add action or policy prediction and conduct closed-loop experiments in simulation and on real hardware.Source: §5 · p. 8
- High-fidelity, semantically aligned Human2Robot videos are presented as a foundation for future video-to-policy inversion and structured supervision.Source: §5 · p. 8
6. Conclusion
Mitty generates robot-execution videos directly from human demonstrations through in-context learning, bypassing keypoints and trajectories. Results across Human2Robot and EPIC-Kitchens show strong performance and generalization, while the work positions video generation as a starting point for future video-to-policy research.
6. Conclusion
- Mitty is a Diffusion Transformer for end-to-end Human2Robot video generation through in-context learning.It bypasses keypoints and trajectories to translate demonstrations into temporally aligned robot-execution videos.Source: §6 · pp. 8–9
- Experiments on Human2Robot and EPIC-Kitchens show strong performance, generalization, and advantages over rendering pipelines and video-editing systems.Ablations highlight the effectiveness of in-context conditioning and mixed training.Source: §6 · p. 9
- High-quality Human2Robot videos offer richer supervision than keypoints or trajectories and form a foundation for future video-to-policy research.The paper identifies reliable video generation as a first step toward end-to-end Human2Robot mapping.Source: §6 · p. 9
A. Evaluation Protocol and Details
The evaluation section covers video-quality metrics, task success, and expert assessment. Task success is judged by whether the robot follows the human reference and completes the intended task.
A. Evaluation Protocol and Details
- The evaluation protocol section covers quantitative metrics, qualitative assessment, and expert evaluation.It introduces the detailed evaluation descriptions presented in the subsections.Source: A. Evaluation Protocol and Details · p. 12
A.1. Evaluation Metric
- FVD measures overall video quality and temporal coherence; PSNR, SSIM, and MSE assess frame-level fidelity and structural consistency.Task Success Rate is evaluated as part of human expert assessment; minor glitches are ignored unless they affect execution, while action mismatches or task incompletion count as failures.Source: §A.1 · p. 12
A.2. Human Expert Evaluation
- Three domain experts independently assign binary success-or-failure judgments to anonymized, shuffled clips, discussing inconsistent labels until consensus.The passage describes this as the structured human evaluation process.Source: §A.2 · p. 12
B. Masquerade Typical Failure Cases
Masquerade exhibits rendering failures and several robot-arm errors, including unreachable placements, arm collisions, and hand-position mistakes. It also handles single-hand scenarios unreliably under its bimanual setup.
B. Masquerade Typical Failure Cases
- Masquerade can fail at rendering with consecutive all-black outputs, and its arm may be placed outside the robot’s feasible workspace.The unreachable placement can leave the arm floating or directed toward an unreachable region, breaking interaction with the target object.Source: B. Masquerade Typical Failure Cases · p. 12
- Incorrect estimates of left- and right-hand spatial relationships can make the rendered robot arms collide, producing unrealistic interactions and invalid manipulation behavior.The passage links these self-collisions to difficulty modeling interarm spatial constraints and coordinated bimanual motion.Source: B. Masquerade Typical Failure Cases · p. 12
- Even with improved hand recognition, localization errors can place the rendered gripper away from the human hand and cause task failure.The cited example describes the gripper appearing on the opposite side of the scene.Source: B. Masquerade Typical Failure Cases · p. 12
- In EPIC-Kitchens, a missing hand detection triggers a predefined neutral pose in the two-arm setup, which can lead to incorrect or failed rendering.The method cannot distinguish genuine one-handed use from transient hand-detector failure, so hiding an undetected arm could also produce errors.Source: B. Masquerade Typical Failure Cases · p. 12
C. Additional Experimental Results
Additional qualitative examples show Mitty maintaining scene structure, motion coherence, and identity stability on Human2Robot tasks. On EPIC-Kitchens, examples from seen and unseen kitchens illustrate robustness to varied scenes and conditions, with more stable results than Masquerade.
C. Additional Experimental Results
- Across grasping, placing, cooking-related actions, countertop manipulation, and tool use, Mitty maintains scene structure, motion coherence, and identity stability.Source: C. Additional Experimental Results · p. 13
- EPIC-Kitchens examples from seen and unseen kitchens demonstrate robustness to camera shake, complex scenes, lighting variation, and multi-object interactions.Source: C. Additional Experimental Results · p. 13
- Side-by-side EPIC-Kitchens comparisons show Masquerade’s hovering, black frames, arm distortion, inpainting errors, and incorrect gripper orientations, while Mitty is more stable under the same conditions.Source: C. Additional Experimental Results · p. 13
D. Our Failure Case Analysis
The failure analysis identifies visual-removal, robot-geometry, and interaction errors in Mitty, while contrasting them with distinct spatial and hand-rendering failures in Masquerade.
D. Our Failure Case Analysis
- Figure 10 groups Mitty’s failures into incomplete erasure, robot-arm structural distortion, and physically unreasonable interaction; structural distortion is most frequent on EPIC-Kitchens.The authors attribute its frequency likely to complex hand–object interactions and challenging first-person motion patterns.Source: D. Our Failure Case Analysis · p. 13, Figure 10 · p. 17
- Unreasonable interactions include implausible motion, missed or drifted targets, and contact with nonexistent objects.Source: D. Our Failure Case Analysis · p. 13, Figure 10 · p. 17
- Masquerade failures include out-of-reach grippers, colliding bimanual arms, and failed hand detection or removal that misrenders the robot arms.Source: Figure 7 · p. 14
- Figure 8 pairs human demonstrations with Mitty outputs; Figure 9 compares inputs, Masquerade outputs, and Mitty outputs.Source: Figure 8 · p. 15, Figure 9 · p. 16