Dynamic Reconstruction of Hand-Object Interaction with Distributed Force-aware Contact Representation
Keywords: Tactile sensing, hand-object tracking and reconstruction
TL;DR: We propose ViTaM-D, a new framework for reconstructing dynamic hand-object interactions with visual and distributed tactile sensing. We also create HOT Dataset for hand-object interaction with deformation and tactile.
Abstract: We present ViTaM-D, a novel visual-tactile framework for dynamic hand-object interaction reconstruction, integrating distributed tactile sensing for more accurate contact modeling. While existing methods focus primarily on visual inputs, they struggle with capturing detailed contact interactions such as object deformation. Our approach leverages distributed tactile sensors to address this limitation by introducing DF-Field. This distributed force-aware contact representation models both kinetic and potential energy in hand-object interaction.
ViTaM-D first reconstructs hand-object interactions using a visual-only network, VDT-Net, and then refines contact details through a force-aware optimization (FO) process, enhancing object deformation modeling. To benchmark our approach, we introduce the HOT dataset, which features 600 sequences of hand-object interactions, including deformable objects, built in a high-precision simulation environment.
Extensive experiments on both the DexYCB and HOT datasets demonstrate significant improvements in accuracy over previous state-of-the-art methods such as gSDF and HOTrack. Our results highlight the superior performance of ViTaM-D in both rigid and deformable object reconstruction, as well as the effectiveness of DF-Field in refining hand poses. This work offers a comprehensive solution to dynamic hand-object interaction reconstruction by seamlessly integrating visual and tactile data. Codes, models, and datasets will be available.
Primary Area: applications to computer vision, audio, language, and other modalities
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 11269
Loading