Object Properties Inferring from and Transfer for Human Interaction Motions

• URL: http://arxiv.org/abs/2008.08999v1
• Date: Thu, 20 Aug 2020 14:36:34 GMT
• Title: Object Properties Inferring from and Transfer for Human Interaction Motions
• Authors: Qian Zheng, Weikai Wu, Hanting Pan, Niloy Mitra, Daniel Cohen-Or, Hui Huang
• Abstract summary: In this paper, we present a fine-grained action recognition method that learns to infer object properties from human interaction motion alone. We collect a large number of videos and 3D skeletal motions of the performing actors using an inertial motion capture device. In particular, we learn to identify the interacting object, by estimating its weight, or its fragility or delicacy.
• Score: 51.896592493436984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans regularly interact with their surrounding objects. Such interactions often result in strongly correlated motion between humans and the interacting objects. We thus ask: "Is it possible to infer object properties from skeletal motion alone, even without seeing the interacting object itself?" In this paper, we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone. This inference allows us to disentangle the motion from the object property and transfer object properties to a given motion. We collected a large number of videos and 3D skeletal motions of the performing actors using an inertial motion capture device. We analyze similar actions and learn subtle differences among them to reveal latent properties of the interacting objects. In particular, we learn to identify the interacting object, by estimating its weight, or its fragility or delicacy. Our results clearly demonstrate that the interaction motions and interacting objects are highly correlated and indeed relative object latent properties can be inferred from the 3D skeleton sequences alone, leading to new synthesis possibilities for human interaction motions. Dataset will be available at http://vcc.szu.edu.cn/research/2020/IT.

Related papers

• PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation [62.53760963292465]
  PhysDreamer is a physics-based approach that endows static 3D objects with interactive dynamics. We present our approach on diverse examples of elastic objects and evaluate the realism of the synthesized interactions through a user study.
  arXiv  Detail & Related papers  (2024-04-19T17:41:05Z)
• 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)
• CG-HOI: Contact-Guided 3D Human-Object Interaction Generation [29.3564427724612]
  We propose CG-HOI, the first method to generate dynamic 3D human-object interactions (HOIs) from text. We model the motion of both human and object in an interdependent fashion, as semantically rich human motion rarely happens in isolation. We show that our joint contact-based human-object interaction approach generates realistic and physically plausible sequences.
  arXiv  Detail & Related papers  (2023-11-27T18:59:10Z)
• Object Motion Guided Human Motion Synthesis [22.08240141115053]
  We study the problem of full-body human motion synthesis for the manipulation of large-sized objects. We propose Object MOtion guided human MOtion synthesis (OMOMO), a conditional diffusion framework. We develop a novel system that captures full-body human manipulation motions by simply attaching a smartphone to the object being manipulated.
  arXiv  Detail & Related papers  (2023-09-28T08:22:00Z)
• GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistency [57.9920824261925]
  Hands are dexterous and highly versatile manipulators that are central to how humans interact with objects and their environment. modeling realistic hand-object interactions is critical for applications in computer graphics, computer vision, and mixed reality. GRIP is a learning-based method that takes as input the 3D motion of the body and the object, and synthesizes realistic motion for both hands before, during, and after object interaction.
  arXiv  Detail & Related papers  (2023-08-22T17:59:51Z)
• NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis [21.650091018774972]
  We create a neural interaction field attached to a specific object, which outputs the distance to the valid interaction manifold given a human pose as input. This interaction field guides the sampling of an object-conditioned human motion diffusion model. We synthesize realistic motions for sitting and lifting with several objects, outperforming alternative approaches in terms of motion quality and successful action completion.
  arXiv  Detail & Related papers  (2023-07-14T17:59:38Z)
• 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)
• IMoS: Intent-Driven Full-Body Motion Synthesis for Human-Object Interactions [69.95820880360345]
  We present the first framework to synthesize the full-body motion of virtual human characters with 3D objects placed within their reach. Our system takes as input textual instructions specifying the objects and the associated intentions of the virtual characters. We show that our synthesized full-body motions appear more realistic to the participants in more than 80% of scenarios.
  arXiv  Detail & Related papers  (2022-12-14T23:59:24Z)

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.