EgoChoir: Capturing 3D Human-Object Interaction Regions from Egocentric Views

• URL: http://arxiv.org/abs/2405.13659v2
• Date: Sun, 13 Oct 2024 05:23:36 GMT
• Title: EgoChoir: Capturing 3D Human-Object Interaction Regions from Egocentric Views
• Authors: Yuhang Yang, Wei Zhai, Chengfeng Wang, Chengjun Yu, Yang Cao, Zheng-Jun Zha,
• Abstract summary: Understanding egocentric human-object interaction (HOI) is a fundamental aspect of human-centric perception. Existing methods primarily leverage observations of HOI to capture interaction regions from an exocentric view. We present EgoChoir, which links object structures with interaction contexts inherent in appearance and head motion to reveal object affordance.
• Score: 51.53089073920215
• License:
• Abstract: Understanding egocentric human-object interaction (HOI) is a fundamental aspect of human-centric perception, facilitating applications like AR/VR and embodied AI. For the egocentric HOI, in addition to perceiving semantics e.g., ''what'' interaction is occurring, capturing ''where'' the interaction specifically manifests in 3D space is also crucial, which links the perception and operation. Existing methods primarily leverage observations of HOI to capture interaction regions from an exocentric view. However, incomplete observations of interacting parties in the egocentric view introduce ambiguity between visual observations and interaction contents, impairing their efficacy. From the egocentric view, humans integrate the visual cortex, cerebellum, and brain to internalize their intentions and interaction concepts of objects, allowing for the pre-formulation of interactions and making behaviors even when interaction regions are out of sight. In light of this, we propose harmonizing the visual appearance, head motion, and 3D object to excavate the object interaction concept and subject intention, jointly inferring 3D human contact and object affordance from egocentric videos. To achieve this, we present EgoChoir, which links object structures with interaction contexts inherent in appearance and head motion to reveal object affordance, further utilizing it to model human contact. Additionally, a gradient modulation is employed to adopt appropriate clues for capturing interaction regions across various egocentric scenarios. Moreover, 3D contact and affordance are annotated for egocentric videos collected from Ego-Exo4D and GIMO to support the task. Extensive experiments on them demonstrate the effectiveness and superiority of EgoChoir.

Related papers

• Grounding 3D Scene Affordance From Egocentric Interactions [52.5827242925951]
  Grounding 3D scene affordance aims to locate interactive regions in 3D environments. We introduce a novel task: grounding 3D scene affordance from egocentric interactions.
  arXiv  Detail & Related papers  (2024-09-29T10:46:19Z)
• EgoGaussian: Dynamic Scene Understanding from Egocentric Video with 3D Gaussian Splatting [95.44545809256473]
  EgoGaussian is a method capable of simultaneously reconstructing 3D scenes and dynamically tracking 3D object motion from RGB egocentric input alone. We show significant improvements in terms of both dynamic object and background reconstruction quality compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-06-28T10:39:36Z)
• Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects [89.95728475983263]
  holistic 3Dunderstanding of such interactions from egocentric views is important for tasks in robotics, AR/VR, action recognition and motion generation. We design the HANDS23 challenge based on the AssemblyHands and ARCTIC datasets with carefully designed training and testing splits. Based on the results of the top submitted methods and more recent baselines on the leaderboards, we perform a thorough analysis on 3D hand(-object) reconstruction tasks.
  arXiv  Detail & Related papers  (2024-03-25T05:12:21Z)
• EgoGen: An Egocentric Synthetic Data Generator [53.32942235801499]
  EgoGen is a new synthetic data generator that can produce accurate and rich ground-truth training data for egocentric perception tasks. At the heart of EgoGen is a novel human motion synthesis model that directly leverages egocentric visual inputs of a virtual human to sense the 3D environment. We demonstrate EgoGen's efficacy in three tasks: mapping and localization for head-mounted cameras, egocentric camera tracking, and human mesh recovery from egocentric views.
  arXiv  Detail & Related papers  (2024-01-16T18:55:22Z)
• LEMON: Learning 3D Human-Object Interaction Relation from 2D Images [56.6123961391372]
  Learning 3D human-object interaction relation is pivotal to embodied AI and interaction modeling. Most existing methods approach the goal by learning to predict isolated interaction elements. We present LEMON, a unified model that mines interaction intentions of the counterparts and employs curvatures to guide the extraction of geometric correlations.
  arXiv  Detail & Related papers  (2023-12-14T14:10:57Z)
• Ego-Body Pose Estimation via Ego-Head Pose Estimation [22.08240141115053]
  Estimating 3D human motion from an egocentric video sequence plays a critical role in human behavior understanding and has various applications in VR/AR. We propose a new method, Ego-Body Pose Estimation via Ego-Head Pose Estimation (EgoEgo), which decomposes the problem into two stages, connected by the head motion as an intermediate representation. This disentanglement of head and body pose eliminates the need for training datasets with paired egocentric videos and 3D human motion.
  arXiv  Detail & Related papers  (2022-12-09T02:25:20Z)
• The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain [20.99718135562034]
  We introduce MECCANO, the first dataset of egocentric videos to study human-object interactions in industrial-like settings. The dataset has been explicitly labeled for the task of recognizing human-object interactions from an egocentric perspective. Baseline results show that the MECCANO dataset is a challenging benchmark to study egocentric human-object interactions in industrial-like scenarios.
  arXiv  Detail & Related papers  (2020-10-12T12:50:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.