VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects

VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/vefx-bench-a-holistic-benchmark-for-generic-video-editing-and-visual-effects

stale

Proof freshness: stale
Proof status: unverified
Display score: 9/10
Last proof check: 2026-04-20
Score updated: 2026-04-20
Score fresh until: 2026-05-20
References: 0
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID vefx-bench-a-holistic-benchmark-for-generic-video-editing-and-visual-effects | Route /signal-canvas/vefx-bench-a-holistic-benchmark-for-generic-video-editing-and-visual-effects

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/vefx-bench-a-holistic-benchmark-for-generic-video-editing-and-visual-effects

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 12

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects

PDF: https://arxiv.org/pdf/2604.16272v1

Source count: 3

Coverage: 50%

Last proof check: 2026-04-20T20:22:59.704Z

Signal Canvas receipt window

Watch and verify: VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects

/buildability/vefx-bench-a-holistic-benchmark-for-generic-video-editing-and-visual-effects

Watchwatch

Subject: VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 11Mixed 1Weak 0

Evidencepartial
We further release VEFX-Bench, a benchmark of 300 curated video-prompt pairs for standardized comparison of editing systems.
Implicationpartial
Directly stated in the abstract.
Verificationpartial
partial
Evidencepartial
Using VEFX-Reward as an evaluator, we benchmark representative commercial and open-source video editing systems, revealing a persistent gap between visual plausibility, instruction following, and edit locality in current models.
Implicationpartial
Directly stated in the abstract as a finding from experiments.
Verificationpartial
partial
Evidencepartial
Existing resources are limited by small scale, missing edited outputs, or the absence of human quality labels
Implicationpartial
Stated in the abstract as motivation for the work.
Verificationpartial
partial
Evidencepartial
It could replace costly manual QA processes and improve the consistency of automated benchmarks within video editing software suites.
Implicationpartial
Implied in the analysis as a potential disruption, but not directly stated in the paper.
Verificationpartial
partial
Evidencepartial
each labeled along three decoupled dimensions: Instruction Following, Rendering Quality, and Edit Exclusivity.
Implicationpartial
Directly stated in the abstract.
Verificationpartial
partial
Evidencepartial
VEFX-Dataset, a human-annotated dataset containing 5,049 video editing examples across 9 major editing categories and 32 subcategories
Implicationpartial
Directly stated in the abstract with specific numbers.
Verificationpartial
partial
Evidencepartial
We introduce VEFX-Dataset, a human-annotated dataset containing 5,049 video editing examples across 9 major editing categories and 32 subcategories
Implicationpartial
Directly stated in the abstract with specific numbers.
Verificationpartial
partial
Evidencepartial
VEFX-Reward jointly processes the source video, the editing instruction, and the edited video, and predicts per-dimension quality scores via ordinal regression.
Implicationpartial
Directly stated in the abstract.
Verificationpartial
partial
Evidencepartial
Experiments show that VEFX-Reward aligns more strongly with human judgments than generic VLM judges and prior reward models on both standard IQA/VQA metrics and group-wise preference evaluation.
Implicationpartial
Directly stated in the abstract and supported by experiments.
Verificationpartial
partial
Evidencepartial
We further release VEFX-Bench, a benchmark of 300 curated video-prompt pairs for standardized comparison of editing systems.
Implicationpartial
Directly stated in the abstract.
Verificationpartial
partial
Evidencepartial
Using VEFX-Reward as an evaluator, we benchmark representative commercial and open-source video editing systems, revealing a persistent gap between visual plausibility, instruction following, and edit locality in current models.
Implicationpartial
Stated in the abstract as a finding from benchmarking.
Verificationpartial
partial
Evidencepartial
Existing resources are limited by small scale, missing edited outputs, or the absence of human quality labels
Implicationpartial
Directly stated in the abstract as motivation.
Verificationpartial
partial

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VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects

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