Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions

• URL: http://arxiv.org/abs/2206.12612v1
• Date: Sat, 25 Jun 2022 09:55:39 GMT
• Title: Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions
• Authors: Weilin Wan, Lei Yang, Lingjie Liu, Zhuoying Zhang, Ruixing Jia, Yi-King Choi, Jia Pan, Christian Theobalt, Taku Komura and Wenping Wang
• Abstract summary: We propose a graph neural network, HO-GCN, to fuse motion data and dynamic descriptors for the prediction task. We show the proposed network that consumes dynamic descriptors can achieve state-of-the-art prediction results and help the network better generalize to unseen objects.
• Score: 82.90906153293585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding human intentions during interactions has been a long-lasting theme, that has applications in human-robot interaction, virtual reality and surveillance. In this study, we focus on full-body human interactions with large-sized daily objects and aim to predict the future states of objects and humans given a sequential observation of human-object interaction. As there is no such dataset dedicated to full-body human interactions with large-sized daily objects, we collected a large-scale dataset containing thousands of interactions for training and evaluation purposes. We also observe that an object's intrinsic physical properties are useful for the object motion prediction, and thus design a set of object dynamic descriptors to encode such intrinsic properties. We treat the object dynamic descriptors as a new modality and propose a graph neural network, HO-GCN, to fuse motion data and dynamic descriptors for the prediction task. We show the proposed network that consumes dynamic descriptors can achieve state-of-the-art prediction results and help the network better generalize to unseen objects. We also demonstrate the predicted results are useful for human-robot collaborations.

Related papers

• Generating Human-Centric Visual Cues for Human-Object Interaction Detection via Large Vision-Language Models [59.611697856666304]
  Human-object interaction (HOI) detection aims at detecting human-object pairs and predicting their interactions. We propose three prompts with VLM to generate human-centric visual cues within an image from multiple perspectives of humans. We develop a transformer-based multimodal fusion module with multitower architecture to integrate visual cue features into the instance and interaction decoders.
  arXiv  Detail & Related papers  (2023-11-26T09:11:32Z)
• Object-centric Video Representation for Long-term Action Anticipation [33.115854386196126]
  Key motivation is that objects provide important cues to recognize and predict human-object interactions. We propose to build object-centric video representations by leveraging visual-language pretrained models. To recognize and predict human-object interactions, we use a Transformer-based neural architecture.
  arXiv  Detail & Related papers  (2023-10-31T22:54:31Z)
• InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion [29.25063155767897]
  This paper addresses a novel task of anticipating 3D human-object interactions (HOIs) Our task is significantly more challenging, as it requires modeling dynamic objects with various shapes, capturing whole-body motion, and ensuring physically valid interactions. Experiments on multiple human-object interaction datasets demonstrate the effectiveness of our method for this task, capable of producing realistic, vivid, and remarkably long-term 3D HOI predictions.
  arXiv  Detail & Related papers  (2023-08-31T17:59:08Z)
• 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)
• 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)
• A Skeleton-aware Graph Convolutional Network for Human-Object Interaction Detection [14.900704382194013]
  We propose a skeleton-aware graph convolutional network for human-object interaction detection, named SGCN4HOI. Our network exploits the spatial connections between human keypoints and object keypoints to capture their fine-grained structural interactions via graph convolutions. It fuses such geometric features with visual features and spatial configuration features obtained from human-object pairs.
  arXiv  Detail & Related papers  (2022-07-11T15:20:18Z)
• GIMO: Gaze-Informed Human Motion Prediction in Context [75.52839760700833]
  We propose a large-scale human motion dataset that delivers high-quality body pose sequences, scene scans, and ego-centric views with eye gaze. Our data collection is not tied to specific scenes, which further boosts the motion dynamics observed from our subjects. To realize the full potential of gaze, we propose a novel network architecture that enables bidirectional communication between the gaze and motion branches.
  arXiv  Detail & Related papers  (2022-04-20T13:17:39Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)
• Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction [24.72947291987545]
  Key challenge for an agent learning to interact with the world is to reason about physical properties of objects. We propose a novel approach for modeling the dynamics of a robot's interactions directly from unlabeled 3D point clouds and images.
  arXiv  Detail & Related papers  (2020-08-02T11:04:49Z)

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.