Generative AI for Accessible and Inclusive Extended Reality

• URL: http://arxiv.org/abs/2410.23803v1
• Date: Thu, 31 Oct 2024 10:43:43 GMT
• Title: Generative AI for Accessible and Inclusive Extended Reality
• Authors: Jens Grubert, Junlong Chen, Per Ola Kristensson,
• Abstract summary: We discuss potential benefits but also challenges that AIGC has for the creation of inclusive and accessible virtual environments. Specifically, we touch upon the decreased need for 3D modeling expertise, benefits of symbolic-only as well as multimodal input, 3D content editing, and 3D model accessibility as well as foundation model-specific challenges.
• Score: 20.669785157017486
• License:
• Abstract: Artificial Intelligence-Generated Content (AIGC) has the potential to transform how people build and interact with virtual environments. Within this paper, we discuss potential benefits but also challenges that AIGC has for the creation of inclusive and accessible virtual environments. Specifically, we touch upon the decreased need for 3D modeling expertise, benefits of symbolic-only as well as multimodal input, 3D content editing, and 3D model accessibility as well as foundation model-specific challenges.

Related papers

• Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model [35.184607650708784]
  Articulate-Anything automates the articulation of diverse, complex objects from many input modalities, including text, images, and videos. Our system exploits existing 3D asset datasets via a mesh retrieval mechanism, along with an actor-critic system that iteratively proposes, evaluates, and refines solutions.
  arXiv  Detail & Related papers  (2024-10-03T19:42:16Z)
• Social Conjuring: Multi-User Runtime Collaboration with AI in Building Virtual 3D Worlds [3.5152339192019113]
  Social Conjurer is a framework for AI-augmented dynamic 3D scene co-creation. This article presents a set of implications for designing human-centered interfaces that incorporate AI models into 3D content generation.
  arXiv  Detail & Related papers  (2024-09-30T23:02:51Z)
• Coral Model Generation from Single Images for Virtual Reality Applications [22.18438294137604]
  This paper introduces a deep-learning framework that generates high-precision 3D coral models from a single image. The project incorporates Explainable AI (XAI) to transform AI-generated models into interactive "artworks"
  arXiv  Detail & Related papers  (2024-09-04T01:54:20Z)
• Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication [50.541882834405946]
  We introduce Atlas3D, an automatic and easy-to-implement text-to-3D method. Our approach combines a novel differentiable simulation-based loss function with physically inspired regularization. We verify Atlas3D's efficacy through extensive generation tasks and validate the resulting 3D models in both simulated and real-world environments.
  arXiv  Detail & Related papers  (2024-05-28T18:33:18Z)
• Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability [118.26563926533517]
  Auto-regressive models have achieved impressive results in 2D image generation by modeling joint distributions in grid space. We extend auto-regressive models to 3D domains, and seek a stronger ability of 3D shape generation by improving auto-regressive models at capacity and scalability simultaneously.
  arXiv  Detail & Related papers  (2024-02-19T15:33:09Z)
• VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Reality [39.53150683721031]
  Our proposed VR-GS system represents a leap forward in human-centered 3D content interaction. The components of our Virtual Reality system are designed for high efficiency and effectiveness.
  arXiv  Detail & Related papers  (2024-01-30T01:28:36Z)
• On the Emergence of Symmetrical Reality [51.21203247240322]
  We introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations. We propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality.
  arXiv  Detail & Related papers  (2024-01-26T16:09:39Z)
• Beyond Reality: The Pivotal Role of Generative AI in the Metaverse [98.1561456565877]
  This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse. We delve into the applications of text generation models like ChatGPT and GPT-3, which are enhancing conversational interfaces with AI-generated characters. We also examine the potential of 3D model generation technologies like Point-E and Lumirithmic in creating realistic virtual objects.
  arXiv  Detail & Related papers  (2023-07-28T05:44:20Z)
• ArK: Augmented Reality with Knowledge Interactive Emergent Ability [115.72679420999535]
  We develop an infinite agent that learns to transfer knowledge memory from general foundation models to novel domains. The heart of our approach is an emerging mechanism, dubbed Augmented Reality with Knowledge Inference Interaction (ArK) We show that our ArK approach, combined with large foundation models, significantly improves the quality of generated 2D/3D scenes.
  arXiv  Detail & Related papers  (2023-05-01T17:57:01Z)
• WenLan 2.0: Make AI Imagine via a Multimodal Foundation Model [74.4875156387271]
  We develop a novel foundation model pre-trained with huge multimodal (visual and textual) data. We show that state-of-the-art results can be obtained on a wide range of downstream tasks.
  arXiv  Detail & Related papers  (2021-10-27T12:25:21Z)
• Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges [16.328866317851187]
  Point cloud processing and 3D shape understanding are challenging tasks for which deep learning techniques have demonstrated great potentials. Here we consider several possible scenarios involving synthetic and real-world point clouds where supervised learning fails due to data scarcity and large domain gaps. We propose to enrich standard feature representations by leveraging self-supervision through a multi-task model that can solve a 3D puzzle while learning the main task of shape classification or part segmentation.
  arXiv  Detail & Related papers  (2020-04-15T23:34:03Z)

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.