Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2404.09833 v1 pith:CE4BLNF6 submitted 2024-04-15 cs.CV cs.AI

classification cs.CVcs.AI
keywords interactivemoduleenvironmentsgamesnerfreal-timerealisticscenes
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generative Physical AI in Vision: A Survey

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A structured review that categorizes physics-aware generative models in vision into explicit-simulation and implicit-learning families and proposes six integration paradigms.

Pith tools