# Precision-Enhanced Human-Object Contact Detection via Depth-Aware Perspective Interaction and Object Texture Restoration

arXiv: https://arxiv.org/abs/2412.09920 · HTML: https://mlsanity.com/paper/2412.09920/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 18 of 18)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [Learning joint reconstruction of hands and manipulated objects](https://mlsanity.com/paper/1904.05767/) (2019)
- [HuMoR: 3D Human Motion Model for Robust Pose Estimation](https://mlsanity.com/paper/2105.04668/) (2021)
- [Resolving 3D Human Pose Ambiguities with 3D Scene Constraints](https://mlsanity.com/paper/1908.06963/) (2019)
- [Understanding Human Hands in Contact at Internet Scale](https://mlsanity.com/paper/2006.06669/) (2020)
- [Visual Semantic Role Labeling](https://mlsanity.com/paper/1505.04474/) (2015)
- [Capturing and Inferring Dense Full-Body Human-Scene Contact](https://mlsanity.com/paper/2206.09553/) (2022)
- [End-to-End Human Object Interaction Detection with HOI Transformer](https://mlsanity.com/paper/2103.04503/) (2021)
- [GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection](https://mlsanity.com/paper/2203.13954/) (2022)
- [PaStaNet: Toward Human Activity Knowledge Engine](https://mlsanity.com/paper/2004.00945/) (2020)
- [ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection](https://mlsanity.com/paper/2008.06254/) (2020)
- [Detecting Human-Object Contact in Images](https://mlsanity.com/paper/2303.03373/) (2023)
- [Detecting Hands and Recognizing Physical Contact in the Wild](https://mlsanity.com/paper/2010.09676/) (2020)
- [HULC: 3D Human Motion Capture with Pose Manifold Sampling and Dense Contact Guidance](https://mlsanity.com/paper/2205.05677/) (2022)
- [ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection](https://mlsanity.com/paper/2109.04047/) (2021)

## Built on by (most cited first, 5 of 5)

- [Prompt Guidance and Human Proximal Perception for HOT Prediction with Regional Joint Loss](https://mlsanity.com/paper/2507.01630/) (2025)
- [QueryCraft: Transformer-Guided Query Initialization for Enhanced Human-Object Interaction Detection](https://mlsanity.com/paper/2508.08590/) (2025)
- [EgoLoc: A Generalizable Solution for Temporal Interaction Localization in Egocentric Videos](https://mlsanity.com/paper/2508.12349/) (2025)
- [What-Meets-Where: Unified Learning of Action and Contact Localization in Images](https://mlsanity.com/paper/2508.09428/) (2025)
- [EgoPHI: Estimating 3D Hand-Object Contact and Force from Egocentric Vision](https://mlsanity.com/paper/2608.13014/) (2026)

## Abstract (arXiv)

Human-object contact (HOT) is designed to accurately identify the areas where humans and objects come into contact. Current methods frequently fail to account for scenarios where objects are frequently blocking the view, resulting in inaccurate identification of contact areas. To tackle this problem, we suggest using a perspective interaction HOT detector called PIHOT, which utilizes a depth map generation model to offer depth information of humans and objects related to the camera, thereby preventing false interaction detection. Furthermore, we use mask dilatation and object restoration techniques to restore the texture details in covered areas, improve the boundaries between objects, and enhance the perception of humans interacting with objects. Moreover, a spatial awareness perception is intended to concentrate on the characteristic features close to the points of contact. The experimental results show that the PIHOT algorithm achieves state-of-the-art performance on three benchmark datasets for HOT detection tasks. Compared to the most recent DHOT, our method enjoys an average improvement of 13%, 27.5%, 16%, and 18.5% on SC-Acc., C-Acc., mIoU, and wIoU metrics, respectively.