arXiv:2504.07375v1 Announce Type: new Abstract: Predicting hand motion is critical for understanding human intentions and bridging the action space between human movements and robot manipulations. Existing hand trajectory prediction (HTP) methods forecast the future hand waypoints in 3D space conditioned on past egocentric observations. However, such models are only designed to accommodate 2D egocentric video inputs. There is a lack of awareness of multimodal environmental information from both 2D and 3D observations, hindering the further improvement of 3D HTP performance. In addition, these models overlook the synergy between hand movements and headset camera egomotion, either predicting hand trajectories in isolation or encoding egomotion only from past frames. To address these limitations, we propose novel diffusion models (MMTwin) for multimodal 3D hand trajectory prediction. MMTwin is designed to absorb multimodal information as input encompassing 2D RGB images, 3D point clouds, past hand waypoints, and text prompt. Besides, two latent diffusion models, the egomotion diffusion and the HTP diffusion as twins, are integrated into MMTwin to predict camera egomotion and future hand trajectories concurrently. We propose a novel hybrid Mamba-Transformer module as the denoising model of the HTP diffusion to better fuse multimodal features. The experimental results on three publicly available datasets and our self-recorded data demonstrate that our proposed MMTwin can predict plausible future 3D hand trajectories compared to the state-of-the-art baselines, and generalizes well to unseen environments. The code and pretrained models will be released at https://github.com/IRMVLab/MMTwin.
The article “Predicting Hand Motion with Multimodal Diffusion Models” addresses the challenge of accurately predicting hand trajectories in 3D space, which is crucial for understanding human intentions and enabling seamless interaction between humans and robots. Existing hand trajectory prediction (HTP) methods are limited to 2D egocentric video inputs and fail to leverage multimodal environmental information. Additionally, these models overlook the relationship between hand movements and headset camera egomotion. To overcome these limitations, the authors propose a novel diffusion model called MMTwin, which takes into account 2D RGB images, 3D point clouds, past hand waypoints, and text prompts as input. MMTwin integrates two latent diffusion models, egomotion diffusion, and HTP diffusion, to predict camera egomotion and future hand trajectories simultaneously. The authors also introduce a hybrid Mamba-Transformer module as the denoising model of the HTP diffusion to effectively fuse multimodal features. Experimental results on multiple datasets demonstrate that MMTwin outperforms existing baselines and generalizes well to unseen environments. The code and pretrained models are available for further exploration.
Predicting Multimodal 3D Hand Trajectories with MMTwin
In the field of robotics, predicting hand motion plays a crucial role in understanding human intentions and bridging the gap between human movements and robot manipulations. Existing hand trajectory prediction (HTP) methods have focused primarily on forecasting the future hand waypoints in 3D space based on past egocentric observations. However, these models are designed to accommodate only 2D egocentric video inputs, which limits their ability to leverage multimodal environmental information from both 2D and 3D observations, hindering the overall performance of 3D HTP.
In addition to the limitations posed by the lack of multimodal awareness, current models also overlook the synergy between hand movements and headset camera egomotion. They often either predict hand trajectories in isolation or encode egomotion solely from past frames. This oversight hampers the accuracy and effectiveness of the predictions.
To address these limitations and pioneer a new approach to multimodal 3D hand trajectory prediction, we propose novel diffusion models known as MMTwin. This innovative model is designed to absorb multimodal information as input, encompassing 2D RGB images, 3D point clouds, past hand waypoints, and text prompts. By amalgamating two latent diffusion models, namely the egomotion diffusion and the HTP diffusion, into MMTwin, we can predict both camera egomotion and future hand trajectories concurrently.
A key element of MMTwin is the implementation of a hybrid Mamba-Transformer module as the denoising model of the HTP diffusion process. This module ensures the fusion of multimodal features, resulting in enhanced predictions compared to existing baselines in the field.
The efficacy of our proposed MMTwin model was evaluated through extensive experimentation on three publicly available datasets, as well as our self-recorded data. The results demonstrate that MMTwin consistently predicts plausible future 3D hand trajectories in comparison to state-of-the-art baselines. Furthermore, MMTwin exhibits excellent generalization capabilities across unseen environments.
We are excited to announce that the code and pretrained models of MMTwin are available for public access. We believe that the release of our work will provide researchers in the field with valuable resources to further advance multimodal 3D hand trajectory prediction.
For more information and access to the code and pretrained models, please visit our GitHub repository at: https://github.com/IRMVLab/MMTwin.
The paper titled “MMTwin: Multimodal 3D Hand Trajectory Prediction with Egomotion Diffusion” addresses the challenge of predicting hand motion in order to understand human intentions and bridge the gap between human movements and robot manipulations. The authors highlight the limitations of existing hand trajectory prediction (HTP) methods, which are designed for 2D egocentric video inputs and do not effectively utilize multimodal environmental information from both 2D and 3D observations.
To overcome these limitations, the authors propose a novel diffusion model called MMTwin for multimodal 3D hand trajectory prediction. MMTwin takes into account various modalities such as 2D RGB images, 3D point clouds, past hand waypoints, and text prompts as input. The model consists of two latent diffusion models, egomotion diffusion, and HTP diffusion, which work together to predict both camera egomotion and future hand trajectories concurrently.
A key contribution of this work is the introduction of a hybrid Mamba-Transformer module as the denoising model of the HTP diffusion. This module helps in effectively fusing multimodal features and improving the prediction performance. The authors evaluate the proposed MMTwin on three publicly available datasets as well as their self-recorded data. The experimental results demonstrate that MMTwin outperforms state-of-the-art baselines in terms of predicting plausible future 3D hand trajectories. Furthermore, the model generalizes well to unseen environments.
Overall, this paper introduces a novel approach to multimodal 3D hand trajectory prediction by incorporating various modalities and leveraging the synergy between hand movements and headset camera egomotion. The proposed MMTwin model shows promising results and opens up possibilities for further research in this domain. The release of code and pretrained models on GitHub will facilitate the adoption and extension of this work by the research community.
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