Zero-shot Text-to-Video Generation

Benchmarks archive 2025-07-28

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Libraries

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Datasets archive 2025-07-28

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Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (5 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator
  25 Sep 2023 2 repositories listed Syntology ran 5 of 11 samples · 6 unverified
  Text-to-video is a rapidly growing research area that aims to generate a semantic, identical, and temporal coherence sequence of frames that accurately align with the input text prompt.
• DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation
  23 May 2023 1 repository listed Syntology ran 3 of 3 samples · 0 unverified
  In the paradigm of AI-generated content (AIGC), there has been increasing attention to transferring knowledge from pre-trained text-to-image (T2I) models to text-to-video (T2V) generation.
• Sketching the Future (STF): Applying Conditional Control Techniques to Text-to-Video Models
  10 May 2023 1 repository listed
  The proliferation of video content demands efficient and flexible neural network based approaches for generating new video content.
• Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
  23 Mar 2023 1 repository listed
  Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.