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 reconstruction accuracy for both rigid and deformable objects. DF-Field also proves more effective in refining hand poses and enhancing contact modeling than previous refinement methods. The code, models, and datasets are available at https://sites.google.com/view/vitam-d/.

Accurate hand motion capture (MoCap) is vital for applications in robotics, virtual reality, and biomechanics, yet existing systems face limitations in capturing high-degree-of-freedom (DoF) joint kinematics and personalized hand shape. Commercial gloves offer up to 21 DoFs, which are insufficient for complex manipulations while neglecting shape variations that are critical for contact-rich tasks. We present FSGlove, an inertial-based system that simultaneously tracks up to 48 DoFs and reconstructs personalized hand shapes via DiffHCal, a novel calibration method. Each finger joint and the dorsum are equipped with IMUs, enabling high-resolution motion sensing. DiffHCal integrates with the parametric MANO model through differentiable optimization, resolving joint kinematics, shape parameters, and sensor misalignment during a single streamlined calibration. The system achieves state-of-the-art accuracy, with joint angle errors of less than 2.7°, and outperforms commercial alternatives in shape reconstruction and contact fidelity. FSGlove’s open-source hardware and software design ensures compatibility with current VR and robotics ecosystems, while its ability to capture subtle motions (e.g., fingertip rubbing) bridges the gap between human dexterity and robotic imitation. Evaluated against Nokov optical MoCap, FSGlove advances hand tracking by unifying the kinematic and contact fidelity. Hardware design, software, and more results are available at: https://sites.google.com/view/fsglove.

Fluid interactions are crucial in daily tasks, with properties like density and viscosity being key parameters. The property states can be used as control signals for robot operation. While density estimation is simple, assessing viscosity, especially for different fluid types, is complex. This study introduces a novel differentiable fitting framework, DiffStir, tailored to identify key physics parameters through stirring. Then, given the estimated physics parameters, we can generate commands to guide the robotic stirring. Comprehensive experiments were conducted to validate the efficacy of DiffStir, showcasing its precision in parameter estimation when benchmarked against reported values in the literature. More experiments and videos can be found in the supplementary materials and on the website: https://diffstir.robotflow.ai.

Garments are important to humans. A visual system that can estimate and track the complete garment pose can be useful for many downstream tasks and real-world applications. In this work, we present a complete package to address the category-level garment pose tracking task: (1) A recording system VR-Garment, with which users can manipulate virtual garment models in simulation through a VR interface. (2) A large-scale dataset VR-Folding, with complex garment pose configurations in manipulation like flattening and folding. (3) An end-to-end online tracking framework GarmentTracking, which predicts complete garment pose both in canonical space and task space given a point cloud sequence. Extensive experiments demonstrate that the proposed GarmentTracking achieves great performance even when the garment has large non-rigid deformation. It outperforms the baseline approach on both speed and accuracy. We hope our proposed solution can serve as a platform for future research. Codes and datasets are available in https://garment-tracking.robotflow.ai.

Garments are important and pervasive in daily life. However, visual analysis on them for pose estimation is challenging because it requires recovering the complete configurations of garments, which is difficult, if not impossible, to annotate in the real world. In this work, we propose a recording system, GarmentTwin, which can track garment poses in dynamic settings such as manipulation. GarmentTwin first collects garment models and RGB-D manipulation videos from the real world and then replays the manipulation process using physics-based animation. This way, we can obtain deformed garments with poses coarsely aligned with real-world observations. Finally, we adopt an optimization-based approach to fit the pose with real-world observations. We verify the fitting results quantitatively and qualitatively. With GarmentTwin, we construct a large-scale dataset named ClothPose, which consists of 30K RGB-D frames from 2400 video clips on 600 garments of 10 categories. We benchmark two tasks on the proposed ClothPose: non-rigid reconstruction and pose estimation. The experiments show that previous baseline methods struggle with highly large non-rigid deformation of manipulated garments. Therefore, we hope that the recording system and the dataset can facilitate research on pose estimation tasks on non-rigid objects. Datasets, models, and codes will be made publicly available.