Video2Game: Real-time, Interactive, Realistic and Browser-Compatible Environment from a Single Video

• URL: http://arxiv.org/abs/2404.09833v1
• Date: Mon, 15 Apr 2024 14:32:32 GMT
• Title: Video2Game: Real-time, Interactive, Realistic and Browser-Compatible Environment from a Single Video
• Authors: Hongchi Xia, Zhi-Hao Lin, Wei-Chiu Ma, Shenlong Wang,
• Abstract summary: Video2Game is a novel approach that automatically converts videos of real-world scenes into realistic and interactive game environments. We show that we can not only produce highly-realistic renderings in real-time, but also build interactive games on top.
• Score: 23.484070818399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Creating high-quality and interactive virtual environments, such as games and simulators, often involves complex and costly manual modeling processes. In this paper, we present Video2Game, a novel approach that automatically converts videos of real-world scenes into realistic and interactive game environments. At the heart of our system are three core components:(i) a neural radiance fields (NeRF) module that effectively captures the geometry and visual appearance of the scene; (ii) a mesh module that distills the knowledge from NeRF for faster rendering; and (iii) a physics module that models the interactions and physical dynamics among the objects. By following the carefully designed pipeline, one can construct an interactable and actionable digital replica of the real world. We benchmark our system on both indoor and large-scale outdoor scenes. We show that we can not only produce highly-realistic renderings in real-time, but also build interactive games on top.

Related papers

• MIMO: Controllable Character Video Synthesis with Spatial Decomposed Modeling [21.1274747033854]
  Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. Milo is a novel framework which can synthesize character videos with controllable attributes. Milo achieves advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes.
  arXiv  Detail & Related papers  (2024-09-24T15:00:07Z)
• Closing the Visual Sim-to-Real Gap with Object-Composable NeRFs [59.12526668734703]
  We introduce Composable Object Volume NeRF (COV-NeRF), an object-composable NeRF model that is the centerpiece of a real-to-sim pipeline. COV-NeRF extracts objects from real images and composes them into new scenes, generating photorealistic renderings and many types of 2D and 3D supervision.
  arXiv  Detail & Related papers  (2024-03-07T00:00:02Z)
• Scaling Face Interaction Graph Networks to Real World Scenes [12.519862235430153]
  We introduce a method which substantially reduces the memory required to run graph-based learned simulators. We show that our method uses substantially less memory than previous graph-based simulators while retaining their accuracy. This paves the way for expanding the application of learned simulators to settings where only perceptual information is available at inference time.
  arXiv  Detail & Related papers  (2024-01-22T14:38:25Z)
• FLARE: Fast Learning of Animatable and Relightable Mesh Avatars [64.48254296523977]
  Our goal is to efficiently learn personalized animatable 3D head avatars from videos that are geometrically accurate, realistic, relightable, and compatible with current rendering systems. We introduce FLARE, a technique that enables the creation of animatable and relightable avatars from a single monocular video.
  arXiv  Detail & Related papers  (2023-10-26T16:13:00Z)
• Neural Assets: Volumetric Object Capture and Rendering for Interactive Environments [8.258451067861932]
  We propose an approach for capturing real-world objects in everyday environments faithfully and fast. We use a novel neural representation to reconstruct effects, such as translucent object parts, and preserve object appearance. This leads to a seamless integration of the proposed neural assets with existing mesh environments and objects.
  arXiv  Detail & Related papers  (2022-12-12T18:55:03Z)
• GeoSim: Photorealistic Image Simulation with Geometry-Aware Composition [81.24107630746508]
  We present GeoSim, a geometry-aware image composition process that synthesizes novel urban driving scenes. We first build a diverse bank of 3D objects with both realistic geometry and appearance from sensor data. The resulting synthetic images are photorealistic, traffic-aware, and geometrically consistent, allowing image simulation to scale to complex use cases.
  arXiv  Detail & Related papers  (2021-01-16T23:00:33Z)
• iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes [54.04456391489063]
  iGibson is a novel simulation environment to develop robotic solutions for interactive tasks in large-scale realistic scenes. Our environment contains fifteen fully interactive home-sized scenes populated with rigid and articulated objects. iGibson features enable the generalization of navigation agents, and that the human-iGibson interface and integrated motion planners facilitate efficient imitation learning of simple human demonstrated behaviors.
  arXiv  Detail & Related papers  (2020-12-05T02:14:17Z)
• ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation [75.0278287071591]
  ThreeDWorld (TDW) is a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. We present initial experiments enabled by TDW in emerging research directions in computer vision, machine learning, and cognitive science.
  arXiv  Detail & Related papers  (2020-07-09T17:33:27Z)
• Learning to Simulate Dynamic Environments with GameGAN [109.25308647431952]
  In this paper, we aim to learn a simulator by simply watching an agent interact with an environment. We introduce GameGAN, a generative model that learns to visually imitate a desired game by ingesting screenplay and keyboard actions during training.
  arXiv  Detail & Related papers  (2020-05-25T14:10:17Z)

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.