DiVR: incorporating context from diverse VR scenes for human trajectory prediction

• URL: http://arxiv.org/abs/2411.08409v1
• Date: Wed, 13 Nov 2024 07:55:41 GMT
• Title: DiVR: incorporating context from diverse VR scenes for human trajectory prediction
• Authors: Franz Franco Gallo, Hui-Yin Wu, Lucile Sassatelli,
• Abstract summary: We propose Diverse Context VR Human Motion Prediction (DiVR), a cross-modal transformer based on the Perceiver architecture. Results show that DiVR achieves higher accuracy and adaptability compared to other models and to static graphs. Our source code is publicly available at https://gitlab.inria.fr/ffrancog/creattive3d-divr-model.
• Score: 2.16656895298847
• License:
• Abstract: Virtual environments provide a rich and controlled setting for collecting detailed data on human behavior, offering unique opportunities for predicting human trajectories in dynamic scenes. However, most existing approaches have overlooked the potential of these environments, focusing instead on static contexts without considering userspecific factors. Employing the CREATTIVE3D dataset, our work models trajectories recorded in virtual reality (VR) scenes for diverse situations including road-crossing tasks with user interactions and simulated visual impairments. We propose Diverse Context VR Human Motion Prediction (DiVR), a cross-modal transformer based on the Perceiver architecture that integrates both static and dynamic scene context using a heterogeneous graph convolution network. We conduct extensive experiments comparing DiVR against existing architectures including MLP, LSTM, and transformers with gaze and point cloud context. Additionally, we also stress test our model's generalizability across different users, tasks, and scenes. Results show that DiVR achieves higher accuracy and adaptability compared to other models and to static graphs. This work highlights the advantages of using VR datasets for context-aware human trajectory modeling, with potential applications in enhancing user experiences in the metaverse. Our source code is publicly available at https://gitlab.inria.fr/ffrancog/creattive3d-divr-model.

Related papers

• Scaling Up Dynamic Human-Scene Interaction Modeling [58.032368564071895]
  TRUMANS is the most comprehensive motion-captured HSI dataset currently available. It intricately captures whole-body human motions and part-level object dynamics. We devise a diffusion-based autoregressive model that efficiently generates HSI sequences of any length.
  arXiv  Detail & Related papers  (2024-03-13T15:45:04Z)
• Learning Interactive Real-World Simulators [96.5991333400566]
  We explore the possibility of learning a universal simulator of real-world interaction through generative modeling. We use the simulator to train both high-level vision-language policies and low-level reinforcement learning policies. Video captioning models can benefit from training with simulated experience, opening up even wider applications.
  arXiv  Detail & Related papers  (2023-10-09T19:42:22Z)
• Learning from synthetic data generated with GRADE [0.6982738885923204]
  We present a framework for generating realistic animated dynamic environments (GRADE) for robotics research. GRADE supports full simulation control, ROS integration, realistic physics, while being in an engine that produces high visual fidelity images and ground truth data. We show that, even training using only synthetic data, can generalize well to real-world images in the same application domain.
  arXiv  Detail & Related papers  (2023-05-07T14:13:04Z)
• User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks [8.513938423514636]
  In this paper, we consider a system consisting of a Metaverse server and multiple VR users. In our multi-user VR scenario for the Metaverse, users have different characteristics and demands for Frames Per Second (FPS) Our proposed user-centric DRL algorithm is called User-centric Critic with Heterogenous Actors (UCHA)
  arXiv  Detail & Related papers  (2023-02-03T00:12:12Z)
• UmeTrack: Unified multi-view end-to-end hand tracking for VR [34.352638006495326]
  Real-time tracking of 3D hand pose in world space is a challenging problem and plays an important role in VR interaction. We present a unified end-to-end differentiable framework for multi-view, multi-frame hand tracking that directly predicts 3D hand pose in world space.
  arXiv  Detail & Related papers  (2022-10-31T19:09:21Z)
• Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World [83.36195426897768]
  We propose VRVO, a novel framework for retrieving the absolute scale from virtual data. We first train a scale-aware disparity network using both monocular real images and stereo virtual data. The resulting scale-consistent disparities are then integrated with a direct VO system.
  arXiv  Detail & Related papers  (2022-03-11T01:51:54Z)
• Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation [98.51313127382937]
  We focus on the use of labels in the synthetic domain alone. Our approach introduces both a way to learn neural-invariant representations and a theoretically inspired view on how to sample the data from the simulator. We showcase our approach on the bird's-eye-view vehicle segmentation task with multi-sensor data.
  arXiv  Detail & Related papers  (2021-11-15T18:37:43Z)
• Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments [66.83839051693695]
  Continual learning refers to the ability of humans and animals to incrementally learn over time in a given environment. We propose to leverage recent advances in 3D virtual environments in order to approach the automatic generation of potentially life-long dynamic scenes with photo-realistic appearance. A novel element of this paper is that scenes are described in a parametric way, thus allowing the user to fully control the visual complexity of the input stream the agent perceives.
  arXiv  Detail & Related papers  (2021-09-16T10:37:21Z)
• DriveGAN: Towards a Controllable High-Quality Neural Simulation [147.6822288981004]
  We introduce a novel high-quality neural simulator referred to as DriveGAN. DriveGAN achieves controllability by disentangling different components without supervision. We train DriveGAN on multiple datasets, including 160 hours of real-world driving data.
  arXiv  Detail & Related papers  (2021-04-30T15:30:05Z)
• SceneGen: Generative Contextual Scene Augmentation using Scene Graph Priors [3.1969855247377827]
  We introduce SceneGen, a generative contextual augmentation framework that predicts virtual object positions and orientations within existing scenes. SceneGen takes a semantically segmented scene as input, and outputs positional and orientational probability maps for placing virtual content. We formulate a novel spatial Scene Graph representation, which encapsulates explicit topological properties between objects, object groups, and the room. To demonstrate our system in action, we develop an Augmented Reality application, in which objects can be contextually augmented in real-time.
  arXiv  Detail & Related papers  (2020-09-25T18:36:27Z)
• AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction [30.61190086847564]
  We propose a generative architecture for multi-future trajectory predictions based on Conditional Variational Recurrent Neural Networks (C-VRNNs) Human interactions are modeled with a graph-based attention mechanism enabling an online attentive hidden state refinement of the recurrent estimation.
  arXiv  Detail & Related papers  (2020-05-17T17:21:23Z)

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.