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arxiv: 2209.14792 · v1 · submitted 2022-09-29 · 💻 cs.CV · cs.AI· cs.LG

Make-A-Video: Text-to-Video Generation without Text-Video Data

Uriel Singer , Adam Polyak , Thomas Hayes , Xi Yin , Jie An , Songyang Zhang , Qiyuan Hu , Harry Yang
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Oron Ashual Oran Gafni Devi Parikh Sonal Gupta Yaniv Taigman
This is my paper

Pith reviewed 2026-05-11 01:08 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG
keywords text-to-video generationtext-to-image modelsunsupervised videospatial-temporal modulesvideo super-resolutiongenerative modelsmotion transfer
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The pith

A method turns text into videos by extending image generators with motion learned separately from unlabeled footage.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows how to move from text-to-image generation to text-to-video generation without starting over or collecting rare paired text-video examples. It trains image and description understanding on text-image pairs, then learns motion dynamics from ordinary video clips that have no text labels. A pipeline of spatial-temporal modules added to existing image models produces the final video frames. This shortcut speeds up training, preserves the creative range of modern image models, and reaches higher resolution, frame rate, and text accuracy than earlier video methods. A reader would care because it suggests video synthesis can scale using data that already exists in large quantities.

Core claim

Make-A-Video decomposes the temporal U-Net and attention tensors into separate spatial and temporal approximations and then runs a spatial-temporal pipeline that includes a video decoder, an interpolation model, and two super-resolution models. The system re-uses a pre-trained text-to-image model for visual content and text alignment while adding motion learned from unsupervised video. The outcome is state-of-the-art text-to-video output in resolution, frame rate, text faithfulness, and overall quality, achieved without any paired text-video training data.

What carries the argument

Spatial-temporal decomposition of U-Net and attention tensors together with a multi-stage pipeline of video decoder, interpolation, and super-resolution models.

If this is right

  • Text-to-video training becomes faster because visual and language representations are reused rather than learned from scratch.
  • Paired text-video datasets are no longer required to reach competitive performance.
  • The generated videos carry over the aesthetic variety and fantastical content already present in current text-to-image systems.
  • High-resolution and high-frame-rate results are produced by chaining the dedicated interpolation and super-resolution stages.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same separation of appearance learning from motion learning could be tried on other data-scarce generation tasks such as 3D or audio synthesis.
  • Modular pipelines like this one may reduce the total compute needed when extending image models to new domains.
  • The approach opens a route to video editing or animation tools that start from a single text prompt and then refine motion independently.

Load-bearing premise

Motion patterns taken from unlabeled video can be added to a text-to-image model through these modules without creating visible motion artifacts or weakening how well the output matches the original text prompt.

What would settle it

A side-by-side evaluation on the same text prompts where Make-A-Video outputs show more flickering, unnatural object trajectories, or lower text-video alignment scores than models trained directly on paired text-video data.

read the original abstract

We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video footage. Make-A-Video has three advantages: (1) it accelerates training of the T2V model (it does not need to learn visual and multimodal representations from scratch), (2) it does not require paired text-video data, and (3) the generated videos inherit the vastness (diversity in aesthetic, fantastical depictions, etc.) of today's image generation models. We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. First, we decompose the full temporal U-Net and attention tensors and approximate them in space and time. Second, we design a spatial temporal pipeline to generate high resolution and frame rate videos with a video decoder, interpolation model and two super resolution models that can enable various applications besides T2V. In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation, as determined by both qualitative and quantitative measures.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes Make-A-Video, a text-to-video generation method that transfers progress from text-to-image (T2I) models by learning appearance and text alignment from paired text-image data while acquiring motion dynamics from unsupervised video footage. It introduces a spatial-temporal decomposition of the U-Net and attention tensors, combined with a multi-stage pipeline (video decoder, temporal interpolation, and super-resolution models) to produce high-resolution, high-frame-rate videos without requiring paired text-video data. The central claim is that this yields state-of-the-art results in spatial/temporal resolution, text faithfulness, and perceptual quality, as measured by both qualitative examples and quantitative metrics.

Significance. If the quantitative claims hold, the work is significant because it demonstrates a practical route to high-quality T2V generation that sidesteps the scarcity of paired text-video data, accelerates training by reusing T2I representations, and inherits the diversity of modern image generators. The decomposition approach and modular pipeline are reusable for other video synthesis tasks and could reduce compute barriers in the field.

major comments (2)
  1. [§4] §4 (Experiments): The SOTA claim is central but rests on quantitative comparisons whose details (specific metrics such as FVD, CLIP similarity, or human preference scores, exact baselines, and effect sizes) are not summarized in the abstract and must be verified against prior T2V methods; without these numbers and ablations on the spatial-temporal modules, the superiority cannot be assessed.
  2. [§3.2] §3.2 (Spatial-Temporal Decomposition): The approximation of full temporal U-Net and attention tensors in space and time is described at a high level; the paper must supply the precise tensor factorization or insertion points (e.g., which layers receive the temporal attention) to confirm that motion transfer occurs without degrading text conditioning or introducing systematic artifacts.
minor comments (2)
  1. [Abstract] The abstract and introduction could more explicitly list the quantitative metrics and baselines used to support the SOTA statement.
  2. [Figures] Figure captions for qualitative results should include the exact text prompts and frame counts to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below with clarifications from the paper and propose targeted revisions to strengthen the presentation of our results and technical details.

read point-by-point responses

  1. Referee: [§4] §4 (Experiments): The SOTA claim is central but rests on quantitative comparisons whose details (specific metrics such as FVD, CLIP similarity, or human preference scores, exact baselines, and effect sizes) are not summarized in the abstract and must be verified against prior T2V methods; without these numbers and ablations on the spatial-temporal modules, the superiority cannot be assessed.

    Authors: We agree that a concise summary of the key quantitative results would improve accessibility. Section 4 reports FVD, CLIP similarity, and human preference scores against baselines including CogVideo and other recent T2V methods, with effect sizes and ablations on the spatial-temporal modules detailed in Tables 1-3 and Section 4.3 (plus appendix). The abstract states the SOTA outcome but does not list the numbers. We will revise the abstract to include a brief summary of the primary metrics and baselines while retaining the existing detailed comparisons in the experiments section. revision: partial

  2. Referee: [§3.2] §3.2 (Spatial-Temporal Decomposition): The approximation of full temporal U-Net and attention tensors in space and time is described at a high level; the paper must supply the precise tensor factorization or insertion points (e.g., which layers receive the temporal attention) to confirm that motion transfer occurs without degrading text conditioning or introducing systematic artifacts.

    Authors: We appreciate this request for greater precision. Section 3.2 describes the decomposition of the U-Net and attention tensors into separate spatial and temporal factors, with temporal attention inserted after spatial attention in the decoder blocks to enable motion modeling while preserving the pretrained text-image conditioning pathway. To address the comment directly, we will add a detailed diagram and explicit layer specifications (including tensor shapes and insertion points) in the revised Section 3.2. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents Make-A-Video as a pipeline that inherits appearance from external pretrained T2I models and motion from separate unsupervised video data. It describes a spatial-temporal decomposition of U-Net/attention tensors plus a multi-stage generation pipeline (video decoder, interpolation, super-resolution). No load-bearing step reduces by construction to a self-fit, self-definition, or self-citation chain; the central claim is a concrete engineering combination of independent pretrained components rather than a tautological prediction. The SOTA assertion rests on external qualitative/quantitative evaluation, not internal re-derivation of inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unproven assumption that motion can be learned independently from appearance using only unlabeled video and that the proposed decomposition sufficiently approximates full spatiotemporal modeling.

axioms (1)
  • domain assumption Decomposing full temporal U-Net and attention tensors into separate spatial and temporal approximations preserves sufficient modeling capacity for coherent video generation.
    Invoked when describing the novel spatial-temporal modules added to the T2I backbone.

pith-pipeline@v0.9.0 · 5588 in / 1198 out tokens · 32678 ms · 2026-05-11T01:08:48.153334+00:00 · methodology

discussion (0)

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