Are Synthetic Data Useful for Egocentric Hand-Object Interaction Detection? An Investigation and the HOI-Synth Domain Adaptation Benchmark

In this study, we investigate the effectiveness of synthetic data in enhancing hand-object interaction detection within the egocentric vision domain. We introduce a simulator able to generate synthetic images of hand-object interactions automatically labeled with hand-object contact states, bounding boxes, and pixel-wise segmentation masks. Through comprehensive experiments and comparative analyses on three egocentric datasets, VISOR, EgoHOS, and ENIGMA-51, we demonstrate that the use of synthetic data and domain adaptation techniques allows for comparable performance to conventional supervised methods while requiring annotations on only a fraction of the real data. When tested with in-domain synthetic data generated from 3D models of real target environments and objects, our best models show consistent performance improvements with respect to standard fully supervised approaches based on labeled real data only. Our study also sets a new benchmark of domain adaptation for egocentric hand-object interaction detection (HOI-Synth) and provides baseline results to encourage the community to engage in this challenging task. We release the generated data, code, and the simulator at the following link: https://iplab.dmi.unict.it/HOI-Synth/.

ENIGMA-51: Towards a Fine-Grained Understanding of Human-Object Interactions in Industrial Scenarios

ENIGMA-51 is a new egocentric dataset acquired in a real industrial domain by 19 subjects who followed instructions to complete the repair of electrical boards using industrial tools (e.g., electric screwdriver) and electronic instruments (e.g., oscilloscope). The 51 sequences are densely annotated with a rich set of labels that enable the systematic study of human-object interactions in the industrial domain. We provide benchmarks on four tasks related to human-object interactions: 1) untrimmed action detection, 2) egocentric human-object interaction detection, 3) short-term object interaction anticipation and 4) natural language understanding of intents and entities. Baseline results show that the ENIGMA-51 dataset poses a challenging benchmark to study human-object interactions in industrial scenarios. We publicly release the dataset at: https://iplab.dmi.unict.it/ENIGMA-51/.

Exploiting Multimodal Synthetic Data for Egocentric Human-Object Interaction Detection in an Industrial Scenario

In this paper, we tackle the problem of Egocentric Human-Object Interaction (EHOI) detection in an industrial setting. To overcome the lack of public datasets in this context, we propose a pipeline and a tool for generating synthetic images of EHOIs paired with several annotations and data signals (e.g., depth maps or instance segmentation masks). Using the proposed pipeline, we present EgoISM-HOI a new multimodal dataset composed of synthetic EHOI images in an industrial environment with rich annotations of hands and objects. To demonstrate the utility and effectiveness of synthetic EHOI data produced by the proposed tool, we designed a new method that predicts and combines different multimodal signals to detect EHOIs in RGB images. Our study shows that exploiting synthetic data to pre-train the proposed method significantly improves performance when tested on real-world data. Moreover, the proposed approach outperforms state-of-the-art class-agnostic methods. To support research in this field, we publicly release the datasets, source code, and pre-trained models at https://iplab.dmi.unict.it/egoism-hoi.

Egocentric Human-Object Interaction Detection Exploiting Synthetic Data

We consider the problem of detecting Egocentric HumanObject Interactions (EHOIs) in industrial contexts. Since collecting and labeling large amounts of real images is challenging, we propose a pipeline and a tool to generate photo-realistic synthetic First Person Vision (FPV) images automatically labeled for EHOI detection in a specific industrial scenario. To tackle the problem of EHOI detection, we propose a method that detects the hands, the objects in the scene, and determines which objects are currently involved in an interaction. We compare the performance of our method with a set of state-of-the-art baselines. Results show that using a synthetic dataset improves the performance of an EHOI detection system, especially when few real data are available. To encourage research on this topic, we publicly release the proposed dataset at the following url: https://iplab.dmi.unict.it/EHOI_SYNTH/.