Pith. sign in

REVIEW 2 cited by

From Sora What We Can See: A Survey of Text-to-Video Generation

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 2405.10674 v1 pith:PJODTOYP submitted 2024-05-17 cs.CV cs.AI

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

With impressive achievements made, artificial intelligence is on the path forward to artificial general intelligence. Sora, developed by OpenAI, which is capable of minute-level world-simulative abilities can be considered as a milestone on this developmental path. However, despite its notable successes, Sora still encounters various obstacles that need to be resolved. In this survey, we embark from the perspective of disassembling Sora in text-to-video generation, and conducting a comprehensive review of literature, trying to answer the question, \textit{From Sora What We Can See}. Specifically, after basic preliminaries regarding the general algorithms are introduced, the literature is categorized from three mutually perpendicular dimensions: evolutionary generators, excellent pursuit, and realistic panorama. Subsequently, the widely used datasets and metrics are organized in detail. Last but more importantly, we identify several challenges and open problems in this domain and propose potential future directions for research and development.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. When Cultures Move: Measuring and Improving Multicultural Text-to-Video Generation

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    Parallel, role-specialized prompt agents improve cultural relevance in text-to-video generation, with a new cross-cultural benchmark showing the largest gains for location cues.

  2. Speaking images. A novel framework for the automated self-description of artworks

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A four-stage open-source AI pipeline turns a digitized artwork into a short video where a depicted person animates and narrates the scene.

Pith tools