Leveraging Synthetic Data for Enhancing Egocentric Hand-Object Interaction Detection

• URL: http://arxiv.org/abs/2603.29733v1
• Date: Tue, 31 Mar 2026 13:32:37 GMT
• Title: Leveraging Synthetic Data for Enhancing Egocentric Hand-Object Interaction Detection
• Abstract summary: This work explores the role of synthetic data in improving the detection of Hand-Object Interactions from egocentric images.<n>By using synthetic data and only 10% of the real labeled data, we achieve improvements in Overall AP over models trained exclusively on real data.
• Score: 23.019466723490297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we explore the role of synthetic data in improving the detection of Hand-Object Interactions from egocentric images. Through extensive experimentation and comparative analysis on VISOR, EgoHOS, and ENIGMA-51 datasets, our findings demonstrate the potential of synthetic data to significantly improve HOI detection, particularly when real labeled data are scarce or unavailable. By using synthetic data and only 10% of the real labeled data, we achieve improvements in Overall AP over models trained exclusively on real data, with gains of +5.67% on VISOR, +8.24% on EgoHOS, and +11.69% on ENIGMA-51. Furthermore, we systematically study how aligning synthetic data to specific real-world benchmarks with respect to objects, grasps, and environments, showing that the effectiveness of synthetic data consistently improves with better synthetic-real alignment. As a result of this work, we release a new data generation pipeline and the new HOI-Synth benchmark, which augments existing datasets with synthetic images of hand-object interaction. These data are automatically annotated with hand-object contact states, bounding boxes, and pixel-wise segmentation masks. All data, code, and tools for synthetic data generation are available at: https://fpv-iplab.github.io/HOI-Synth/.

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