Hyokeun Lee

Woojin Cho, Jihyun Lee, Minjae Yi et al.

Existing datasets for 3D hand-object interaction are limited either in the data cardinality, data variations in interaction scenarios, or the quality of annotations. In this work, we present a comprehensive new training dataset for hand-object interaction called HOGraspNet. It is the only real dataset that captures full grasp taxonomies, providing grasp annotation and wide intraclass variations. Using grasp taxonomies as atomic actions, their space and time combinatorial can represent complex hand activities around objects. We select 22 rigid objects from the YCB dataset and 8 other compound objects using shape and size taxonomies, ensuring coverage of all hand grasp configurations. The dataset includes diverse hand shapes from 99 participants aged 10 to 74, continuous video frames, and a 1.5M RGB-Depth of sparse frames with annotations. It offers labels for 3D hand and object meshes, 3D keypoints, contact maps, and \emph{grasp labels}. Accurate hand and object 3D meshes are obtained by fitting the hand parametric model (MANO) and the hand implicit function (HALO) to multi-view RGBD frames, with the MoCap system only for objects. Note that HALO fitting does not require any parameter tuning, enabling scalability to the dataset's size with comparable accuracy to MANO. We evaluate HOGraspNet on relevant tasks: grasp classification and 3D hand pose estimation. The result shows performance variations based on grasp type and object class, indicating the potential importance of the interaction space captured by our dataset. The provided data aims at learning universal shape priors or foundation models for 3D hand-object interaction. Our dataset and code are available at https://hograspnet2024.github.io/.

Younghoon Ko, Hyemin Park, Hyuk-Jae Lee et al.

As the memory channel count is confined by physical dimensions, memory expanders appear to be a promising approach to extending memory capacity and channels by augmenting the existing I/O interface (e.g., PCIe) with memory-semantic protocols like CXL. Unfortunately, the physical constraints of a computing system restrict scalable capacity expansion with memory expanders. In this work, we propose a block-level compression scheme for modern memory expanders, IBEX, to achieve larger effective memory capacity. Given the performance overhead associated with block-level compression algorithms (e.g., LZ77), IBEX employs a promotion-based approach: only cold data is compressed, whereas hot data remains uncompressed. Our key innovation is internal bandwidth-efficient block management that precisely identifies cold pages with minimal metadata access overhead. Still, the promotion-based approach poses several performance-related challenges at the design level. Therefore, we also propose a shadowed promotion scheme that temporarily postpones the deallocation of promoted data, thereby mitigating the performance penalty incurred by demotion (i.e., recompression). Furthermore, we optimize our compression scheme by compacting metadata and co-locating multiple target blocks for efficient bandwidth utilization. Consequently, IBEX achieves an average of 1.28x-1.40x speedups compared to the state-of-the-art promotion-based block-level approaches. We open-source IBEX at https://github.com/relacslab/ibex-ics26.