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Forecasting Bimanual Object Manipulation Sequences from Unimanual Observations
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Proposes a graph-GRU forecaster with an object motion module that zeroes out non-manipulated object motion and a human pose ensemble module that uses GrabNet to refine arms toward hand-object contact, for forecasting bimanual manipulation from unimanual observations.
Problem
Forecasting bimanual object manipulation sequences (human and object motion) when only unimanual observations are available, while maintaining realistic hand-object contact and avoiding floating or drifting objects.
Approach
Factorizes reconstruction and forecasting into object motion prediction with binary gating (stationary vs moving) followed by human pose refinement conditioned on the object in contact, using a GrabNet-based decoder selected by Gumbel-Softmax contact probabilities, instead of a plain MLP decoder.
Key results
- On KIT MoCap, the method's overall forecasting error is 33.7 mm versus 51.3 mm for Mod, with distractor error 4.9 mm versus 27.5 mm.
- On KIT RGBD, overall forecasting error is 25.8 cm versus 44.3 cm for the Mod baseline (reconstruction overall 8.7 cm vs 17.7 cm).
- Contact classification accuracy is 90.1% (forecasting) on KIT MoCap and 84.0% on KIT RGBD.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use the OMM and PEM rather than removing both modules (ablation) | Bimanual forecasting method · KIT MoCap forecasting (0:2) | Overall error (mm): 52.7 -> 33.7 | Helped |
| use the OMM and PEM rather than removing both modules (ablation) | Bimanual forecasting method · KIT RGBD forecasting (0:2) | Overall error (cm): 46.3 -> 25.8 | Helped |
| use ground-truth object motion and contact labels (ablation) | Bimanual forecasting method · KIT MoCap forecasting (0:2) | Overall error (mm): 33.7 -> 32.1 | Helped |
| use ground-truth object motion and contact labels (ablation) | Bimanual forecasting method · KIT RGBD forecasting (0:2) | Overall error (cm): 25.8 -> 24.1 | Helped |
| add the proposed OMM and PEM | Bimanual forecasting method · KIT MoCap forecasting (0:2) | Overall error (mm): 51.3 -> 33.7 | Helped |
| add the proposed OMM and PEM | Bimanual forecasting method · KIT RGBD forecasting (0:2) | Overall error (cm): 44.3 -> 25.8 | Helped |
Surprising findings
- A simple generic graph GRU with the proposed modules outperforms more sophisticated graph architectures, suggesting architectural complexity does not necessarily yield more plausible outputs.
- Removing the proposed components (w/o OMM + PEM) yields errors worse than the prior SOTA Mod.
Limitations
- Unimanual inputs were generated by masking bimanual data rather than recording people with one functional arm.
- KIT RGBD requires estimated contact labels and noisy detections, degrading performance.
- Forecasting assumes mostly deterministic motion over short horizons.
Experimental setup
Datasets: KIT Bimanual Manipulation (KIT MoCap) KIT RGBD Bimanual Actions (KIT RGBD)
Benchmarks: KIT MoCap KIT RGBD
Metrics: Mean Per Joint Position Error (distractor, contact, overall, contact classification accuracy
Baselines: CAHMP MTGCN Mod (Razali and Demiris 2023)
Builds on: GrabNet Graph GRU Gumbel-Softmax MTGCN CAHMP
Closest papers by meaning
- BimArt: A Unified Approach for the Synthesis of 3D Bimanual Interaction with Articulated Objects (CVPR 2025 · similarity 0.87)
- ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning (CVPR 2025 · similarity 0.87)
- OAKINK2: A Dataset of Bimanual Hands-Object Manipulation in Complex Task Completion (CVPR 2024 · similarity 0.86)
- TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding (CVPR 2024 · similarity 0.86)
- Forecasting of 3D Whole-body Human Poses with Grasping Objects (CVPR 2024 · similarity 0.86)
- MotionMap: Representing Multimodality in Human Pose Forecasting (CVPR 2025 · similarity 0.84)
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