Joint Reconstruction of 3D Human and Object via Contact-Based Refinement Transformer
- URL: http://arxiv.org/abs/2404.04819v1
- Date: Sun, 7 Apr 2024 06:01:49 GMT
- Title: Joint Reconstruction of 3D Human and Object via Contact-Based Refinement Transformer
- Authors: Hyeongjin Nam, Daniel Sungho Jung, Gyeongsik Moon, Kyoung Mu Lee,
- Abstract summary: We present a novel joint 3D human-object reconstruction method (CONTHO) that effectively exploits contact information between humans and objects.
There are two core designs in our system: 1) 3D-guided contact estimation and 2) contact-based 3D human and object refinement.
- Score: 58.98785899556135
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Human-object contact serves as a strong cue to understand how humans physically interact with objects. Nevertheless, it is not widely explored to utilize human-object contact information for the joint reconstruction of 3D human and object from a single image. In this work, we present a novel joint 3D human-object reconstruction method (CONTHO) that effectively exploits contact information between humans and objects. There are two core designs in our system: 1) 3D-guided contact estimation and 2) contact-based 3D human and object refinement. First, for accurate human-object contact estimation, CONTHO initially reconstructs 3D humans and objects and utilizes them as explicit 3D guidance for contact estimation. Second, to refine the initial reconstructions of 3D human and object, we propose a novel contact-based refinement Transformer that effectively aggregates human features and object features based on the estimated human-object contact. The proposed contact-based refinement prevents the learning of erroneous correlation between human and object, which enables accurate 3D reconstruction. As a result, our CONTHO achieves state-of-the-art performance in both human-object contact estimation and joint reconstruction of 3D human and object. The code is publicly available at https://github.com/dqj5182/CONTHO_RELEASE.
Related papers
- Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models [8.933560282929726]
We introduce a novel affordance representation, named Comprehensive Affordance (ComA)
Given a 3D object mesh, ComA models the distribution of relative orientation and proximity of vertices in interacting human meshes.
We demonstrate that ComA outperforms competitors that rely on human annotations in modeling contact-based affordance.
arXiv Detail & Related papers (2024-01-23T18:59:59Z) - Primitive-based 3D Human-Object Interaction Modelling and Programming [59.47308081630886]
We propose a novel 3D geometric primitive-based language to encode both humans and objects.
We build a new benchmark on 3D HAOI consisting of primitives together with their images.
We believe this primitive-based 3D HAOI representation would pave the way for 3D HAOI studies.
arXiv Detail & Related papers (2023-12-17T13:16:49Z) - DECO: Dense Estimation of 3D Human-Scene Contact In The Wild [54.44345845842109]
We train a novel 3D contact detector that uses both body-part-driven and scene-context-driven attention to estimate contact on the SMPL body.
We significantly outperform existing SOTA methods across all benchmarks.
We also show qualitatively that DECO generalizes well to diverse and challenging real-world human interactions in natural images.
arXiv Detail & Related papers (2023-09-26T21:21:07Z) - Detecting Human-Object Contact in Images [75.35017308643471]
Humans constantly contact objects to move and perform tasks.
There exists no robust method to detect contact between the body and the scene from an image.
We build a new dataset of human-object contacts for images.
arXiv Detail & Related papers (2023-03-06T18:56:26Z) - 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) - Human-Aware Object Placement for Visual Environment Reconstruction [63.14733166375534]
We show that human-scene interactions can be leveraged to improve the 3D reconstruction of a scene from a monocular RGB video.
Our key idea is that, as a person moves through a scene and interacts with it, we accumulate HSIs across multiple input images.
We show that our scene reconstruction can be used to refine the initial 3D human pose and shape estimation.
arXiv Detail & Related papers (2022-03-07T18:59:02Z) - Neural Free-Viewpoint Performance Rendering under Complex Human-object
Interactions [35.41116017268475]
4D reconstruction of human-object interaction is critical for immersive VR/AR experience and human activity understanding.
Recent advances still fail to recover fine geometry and texture results from sparse RGB inputs, especially under challenging human-object interactions scenarios.
We propose a neural human performance capture and rendering system to generate both high-quality geometry and photo-realistic texture of both human and objects.
arXiv Detail & Related papers (2021-08-01T04:53:54Z)
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.