CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement

• URL: http://arxiv.org/abs/2406.19353v1
• Date: Thu, 27 Jun 2024 17:32:18 GMT
• Title: CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement
• Authors: Chengwen Zhang, Yun Liu, Ruofan Xing, Bingda Tang, Li Yi,
• Abstract summary: We present CORE4D, a novel large-scale 4D human-object collaborative object rearrangement. With 1K human-object-human motion sequences captured in the real world, we enrich CORE4D by contributing an iterative collaboration strategy to augment motions to a variety of novel objects. Benefiting from extensive motion patterns provided by CORE4D, we benchmark two tasks aiming at generating human-object interaction: human-object motion forecasting and interaction synthesis.
• Score: 20.520938266527438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding how humans cooperatively rearrange household objects is critical for VR/AR and human-robot interaction. However, in-depth studies on modeling these behaviors are under-researched due to the lack of relevant datasets. We fill this gap by presenting CORE4D, a novel large-scale 4D human-object-human interaction dataset focusing on collaborative object rearrangement, which encompasses diverse compositions of various object geometries, collaboration modes, and 3D scenes. With 1K human-object-human motion sequences captured in the real world, we enrich CORE4D by contributing an iterative collaboration retargeting strategy to augment motions to a variety of novel objects. Leveraging this approach, CORE4D comprises a total of 11K collaboration sequences spanning 3K real and virtual object shapes. Benefiting from extensive motion patterns provided by CORE4D, we benchmark two tasks aiming at generating human-object interaction: human-object motion forecasting and interaction synthesis. Extensive experiments demonstrate the effectiveness of our collaboration retargeting strategy and indicate that CORE4D has posed new challenges to existing human-object interaction generation methodologies. Our dataset and code are available at https://github.com/leolyliu/CORE4D-Instructions.

Related papers

• ParaHome: Parameterizing Everyday Home Activities Towards 3D Generative Modeling of Human-Object Interactions [11.32229757116179]
  We introduce the ParaHome system, designed to capture dynamic 3D movements of humans and objects within a common home environment. By leveraging the ParaHome system, we collect a novel large-scale dataset of human-object interaction.
  arXiv  Detail & Related papers  (2024-01-18T18:59:58Z)
• HODN: Disentangling Human-Object Feature for HOI Detection [51.48164941412871]
  We propose a Human and Object Disentangling Network (HODN) to model the Human-Object Interaction (HOI) relationships explicitly. Considering that human features are more contributive to interaction, we propose a Human-Guide Linking method to make sure the interaction decoder focuses on the human-centric regions. Our proposed method achieves competitive performance on both the V-COCO and the HICO-Det Linking datasets.
  arXiv  Detail & Related papers  (2023-08-20T04:12:50Z)
• Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations [61.659439423703155]
  TOHO: Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations. Our method generates continuous motions that are parameterized only by the temporal coordinate. This work takes a step further toward general human-scene interaction simulation.
  arXiv  Detail & Related papers  (2023-03-23T09:31:56Z)
• Full-Body Articulated Human-Object Interaction [61.01135739641217]
  CHAIRS is a large-scale motion-captured f-AHOI dataset consisting of 16.2 hours of versatile interactions. CHAIRS provides 3D meshes of both humans and articulated objects during the entire interactive process. By learning the geometrical relationships in HOI, we devise the very first model that leverage human pose estimation.
  arXiv  Detail & Related papers  (2022-12-20T19:50:54Z)
• Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors [42.17542596399014]
  We present a method for inferring diverse 3D models of human-object interactions from images. Our method extracts high-level commonsense knowledge from large language models. We quantitatively evaluate the inferred 3D models on a large human-object interaction dataset.
  arXiv  Detail & Related papers  (2022-09-06T13:32:55Z)
• Compositional Human-Scene Interaction Synthesis with Semantic Control [16.93177243590465]
  We aim to synthesize humans interacting with a given 3D scene controlled by high-level semantic specifications. We design a novel transformer-based generative model, in which the articulated 3D human body surface points and 3D objects are jointly encoded. Inspired by the compositional nature of interactions that humans can simultaneously interact with multiple objects, we define interaction semantics as the composition of varying numbers of atomic action-object pairs.
  arXiv  Detail & Related papers  (2022-07-26T11:37:44Z)
• BEHAVE: Dataset and Method for Tracking Human Object Interactions [105.77368488612704]
  We present the first full body human- object interaction dataset with multi-view RGBD frames and corresponding 3D SMPL and object fits along with the annotated contacts between them. We use this data to learn a model that can jointly track humans and objects in natural environments with an easy-to-use portable multi-camera setup.
  arXiv  Detail & Related papers  (2022-04-14T13:21:19Z)
• H4D: Human 4D Modeling by Learning Neural Compositional Representation [75.34798886466311]
  This work presents a novel framework that can effectively learn a compact and compositional representation for dynamic human. A simple yet effective linear motion model is proposed to provide a rough and regularized motion estimation. Experiments demonstrate our method is not only efficacy in recovering dynamic human with accurate motion and detailed geometry, but also amenable to various 4D human related tasks.
  arXiv  Detail & Related papers  (2022-03-02T17:10:49Z)
• Estimating 3D Motion and Forces of Human-Object Interactions from Internet Videos [49.52070710518688]
  We introduce a method to reconstruct the 3D motion of a person interacting with an object from a single RGB video. Our method estimates the 3D poses of the person together with the object pose, the contact positions and the contact forces on the human body.
  arXiv  Detail & Related papers  (2021-11-02T13:40:18Z)

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.