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Anran Xu

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Computer Vision Human-Computer Interaction Artificial Intelligence

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preprint2026arXiv

HapticLDM: A Diffusion Model for Text-to-Vibrotactile Generation

Text-to-vibration generation converts natural language into haptic feedback, enabling vibration-effect designers to get scenarios-fitted vibrations more efficiently, which shows great potentials in application fields such as metaverse, games, and film to enrich the user experience in interactive scenarios. The core challenge in this field is how to generate accurate, consistent, and complete vibrations according to textual semantics. Very recent autoregressive (AR) approaches (e.g., HapticGen) exhibit limited capacity in fully capturing global dependencies, owing to the inherent sequential nature of their modeling and prevailing data constraints. In this paper, we proposed HapticLDM, the first text-to-vibration generative model built upon Latent Diffusion Models (LDMs). Firstly, with respect to the data, we introduced a text-processing strategy that emphasizes dynamic characteristics to curate high-quality data pairs for fine-grained dynamic modeling. Secondly, HapticLDM incorporates a global denoising mechanism that regulates coherent and stable variations in the temporal envelope. Furthermore, we conduct extensive evaluations, including A/B testing against the state-of-the-art baseline and a user study involving 30 participants. The results demonstrate that our model enhances realism and semantic alignment. Qualitative feedback further indicates that HapticLDM simplifies the haptic design workflow while generating diverse, subtle, and physically precise vibrations.

preprint2022arXiv

OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction

Learning how humans manipulate objects requires machines to acquire knowledge from two perspectives: one for understanding object affordances and the other for learning human's interactions based on the affordances. Even though these two knowledge bases are crucial, we find that current databases lack a comprehensive awareness of them. In this work, we propose a multi-modal and rich-annotated knowledge repository, OakInk, for visual and cognitive understanding of hand-object interactions. We start to collect 1,800 common household objects and annotate their affordances to construct the first knowledge base: Oak. Given the affordance, we record rich human interactions with 100 selected objects in Oak. Finally, we transfer the interactions on the 100 recorded objects to their virtual counterparts through a novel method: Tink. The recorded and transferred hand-object interactions constitute the second knowledge base: Ink. As a result, OakInk contains 50,000 distinct affordance-aware and intent-oriented hand-object interactions. We benchmark OakInk on pose estimation and grasp generation tasks. Moreover, we propose two practical applications of OakInk: intent-based interaction generation and handover generation. Our datasets and source code are publicly available at https://github.com/lixiny/OakInk.

preprint2022arXiv

SyncUp: Vision-based Practice Support for Synchronized Dancing

The beauty of synchronized dancing lies in the synchronization of body movements among multiple dancers. While dancers utilize camera recordings for their practice, standard video interfaces do not efficiently support their activities of identifying segments where they are not well synchronized. This thus fails to close a tight loop of an iterative practice process (i.e., capturing a practice, reviewing the video, and practicing again). We present SyncUp, a system that provides multiple interactive visualizations to support the practice of synchronized dancing and liberate users from manual inspection of recorded practice videos. By analyzing videos uploaded by users, SyncUp quantifies two aspects of synchronization in dancing: pose similarity among multiple dancers and temporal alignment of their movements. The system then highlights which body parts and which portions of the dance routine require further practice to achieve better synchronization. The results of our system evaluations show that our pose similarity estimation and temporal alignment predictions were correlated well with human ratings. Participants in our qualitative user evaluation expressed the benefits and its potential use of SyncUp, confirming that it would enable quick iterative practice.

preprint2020arXiv

Off-Road Drivable Area Extraction Using 3D LiDAR Data

We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application. A specific deep learning framework is designed to deal with the ambiguous area, which is one of the main challenges in the off-road environment. To reduce the considerable demand for human-annotated data for network training, we utilize the information from vast quantities of vehicle paths and auto-generated obstacle labels. Using these autogenerated annotations, the proposed network can be trained using weakly supervised or semi-supervised methods, which can achieve better performance with fewer human annotations. The experiments on our dataset illustrate the reasonability of our framework and the validity of our weakly and semi-supervised methods.