Dynamic Reconstruction of Hand-Object Interaction with Distributed Force-aware Contact Representation
Zhenjun Yu, Wenqiang Xu, Pengfei Xie, Yutong Li, Brian W. Anthony, Zhuorui Zhang, Cewu Lu
TL;DR
ViTaM-D is a new visual-tactile framework that reconstructs dynamic hand-object interactions by combining visual data with force-aware contact modeling, improving accuracy especially for deformable objects.
Contribution
Introduces DF-Field, a force-aware contact representation, and a combined visual-tactile framework ViTaM-D for enhanced interaction reconstruction.
Findings
Outperforms state-of-the-art methods in reconstruction accuracy.
Effectively models deformable object interactions.
Provides a new HOT dataset for deformable object evaluation.
Abstract
We present ViTaM-D, a novel visual-tactile framework for reconstructing dynamic hand-object interaction with distributed tactile sensing to enhance contact modeling. Existing methods, relying solely on visual inputs, often fail to capture occluded interactions and object deformation. To address this, we introduce DF-Field, a distributed force-aware contact representation leveraging kinetic and potential energy in hand-object interactions. ViTaM-D first reconstructs interactions using a visual network with contact constraint, then refines contact details through force-aware optimization, improving object deformation modeling. To evaluate deformable object reconstruction, we introduce the HOT dataset, featuring 600 hand-object interaction sequences in a high-precision simulation environment. Experiments on DexYCB and HOT datasets show that ViTaM-D outperforms state-of-the-art methods in…
Peer Reviews
Decision·ICLR 2025 Conference Withdrawn Submission
1. **Novel Integration of Tactile Information**: By combining conventional visual reconstruction with tactile data, the authors effectively address limitations seen in purely visual methods, where contact precision often depends on inferred data. The use of tactile information in DF-Field provides direct contact modeling, introducing additional "knowledge" that improves interaction fidelity. 2. **Innovative DF-Field and Optimization**: The DF-Field approach, coupled with force-aware optimization
1. **Clarification on Real-World Applicability**: The authors could enhance the paper by explaining the practical contexts where distributed tactile sensors would be available, and the types of real-world or robotic scenarios that could leverage both point cloud data and tactile sensing. 2. **Justifying Tactile Use Beyond Visual Information**: While the authors use tactile data to refine hand poses initially estimated by the network, it would strengthen the paper to add more experiments to compa
- The paper proposes a new dataset. Compared to existing work such as DexYCB which includes hand-object interaction with only rigid objects, the proposed HOT dataset includes both rigid and deformable objects, which is of potential value to the research community towards more general deformable objects. - The paper seems to be in the right direction to model generic hand-object interaction that includes both rigid and deformable objects. - Qualitative figures illustrate their points pretty well.
- **The writing, or the presentation, of the paper requires a significant amount of effort to meet the standard of an ICLR paper.** This is a major weakness in the current version of the paper. The writing makes it hard for the audience to understand what the technical contributions are. To enumerate a few points: - The authors proposed a confusing amount of acronyms in the paper. To name a few, DF-Field, ViTaM-D, VDT-Net, FO. Some of these acronyms are used abruptly without introducing the
1. This work focuses on using combined visual-tactile information to achieve more accurate hand-object 3D reconstruction, which is valuable but underexplored at present, especially when the grasped objects are deformable. 2. This work attempts to introduce new tactile related force representation DF-Field by leveraging kinetic and potential energy theory to model hand-object contact attributes and object deformation.
1. The definition of the tactile related force representation DF-Field seems somewhat rough. The presentations lack of detailed theoretical analysis from the perspective of physical laws, but directly define the formulas (1) and (2). That might lead to a weak support for the effective utilization of the tactile information. 2. The cascaded mode of fusing visual-tactile information lacks novelty, ignoring the real-time information fusion of the visual and tactile perception data. According to th
1. This paper proposes to integrate tactile data and tactile representation for modeling accurate contact, which is underexplored approach in hand-object reconstruction 2. The proposed method is easy to understand and well-written in terms of readability. 3. The VDT+FO achieves SOTA performance compared to RGB-based counterpart gSDF and point-based counterpart HOTrack.
1. Although the paper claims that it reconstructs hand and object from visual data, their visual input consists of streams of 3D point clouds (unlike RGB visual data from gSDF). This is hardly visual data, as most papers refer to visual data as RGB input data. Such terminology may, hence mislead many of the readers. 2. Despite multiple previous methods on how to effectively optimize hand and object based on novel contact representation such as ContactOpt (Grady et al., CVPR 2021), TOCH (Zhou et
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHand Gesture Recognition Systems · Robot Manipulation and Learning · Teleoperation and Haptic Systems
MethodsFocus