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docs: publish v1.0 protocol and reproducibility metadata

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- # VEFX-Bench: Video Editing Quality Benchmark
 
 
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  VEFX-Bench is a benchmark dataset for evaluating video editing model quality. It contains **300 original source videos** with editing instructions across **9 task categories**.
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  ## Dataset Structure
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  - `0000.mp4` through `0299.mp4`: 300 original source videos (720p)
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- - `prompts.json`: Editing instructions indexed by video number
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- - `benchmark_meta.json`: Metadata including category labels and filename mapping
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  ## Usage
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- 1. Download all 300 videos and `prompts.json`
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- 2. Apply your video editing model following each prompt
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- 3. Save edited videos as `0000.mp4` through `0299.mp4` (matching index)
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- 4. Submit the zip to the [VEFX-Leaderboard](https://github.com/YOUR_ORG/VEFX-Leaderboard)
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  ## Evaluation Dimensions
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- - **IF (Instructional Following)**: How well the edit follows the instruction (1-4)
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- - **RQ (Render Quality)**: Visual quality and temporal consistency (1-4)
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- - **EE (Edit Exclusivity)**: Preservation of unrelated content (1-4)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Task Categories
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  | Category | Count |
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- |----------|-------|
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- | Style Transfer | varies |
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- | Object Manipulation | varies |
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- | Background Change | varies |
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- | Color/Lighting | varies |
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- | Motion/Animation | varies |
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- | Text/Overlay | varies |
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- | Composition | varies |
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- | Removal/Inpainting | varies |
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- | Complex/Multi-step | varies |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Citation
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+ # VEFX-Bench v1.0: Video Editing Quality Benchmark
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+
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+ > **Frozen release:** VEFX-Bench v1.0 — frozen on 2026-08-03. The immutable data and evaluator identifiers are listed under [Reproducibility](#reproducibility).
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  VEFX-Bench is a benchmark dataset for evaluating video editing model quality. It contains **300 original source videos** with editing instructions across **9 task categories**.
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  ## Dataset Structure
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  - `0000.mp4` through `0299.mp4`: 300 original source videos (720p)
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+ - `prompts.json`: editing instructions indexed by video number
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+ - `benchmark_meta.json`: the v1.0 manifest with category labels and filename mapping
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  ## Usage
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+ 1. Download the frozen v1.0 revision listed below.
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+ 2. Apply your video editing model following each prompt.
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+ 3. Save edited videos as `0000.mp4` through `0299.mp4` (matching index).
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+ 4. Submit the zip to the [VEFX-Leaderboard](https://github.com/XiangboGaoBarry/VEFX-Leaderboard).
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  ## Evaluation Dimensions
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+ - **IF (Instructional Following):** how well the edit follows the instruction (14)
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+ - **RQ (Render Quality):** visual quality and temporal consistency (14)
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+ - **EE (Edit Exclusivity):** preservation of unrelated content (14)
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+
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+ ## Overall and Aggregation Order
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+
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+ **Overall is the primary GeoAgg ranking metric; it is not the arithmetic average of IF, RQ, and EE.** For item \(j\), first normalize each soft dimension score to \([0,1]\):
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+
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+ \[
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+ i_j=\frac{IF_j-1}{3},\qquad r_j=\frac{RQ_j-1}{3},\qquad e_j=\frac{EE_j-1}{3}.
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+ \]
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+
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+ Then compute the item-level weighted geometric aggregate, with IF weighted twice:
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+
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+ \[
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+ g_j=1+3\left(i_j^2r_je_j\right)^{1/4}.
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+ \]
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+
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+ Finally, average the 300 item-level values:
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+
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+ \[
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+ \mathrm{Overall}=\frac{1}{300}\sum_{j=1}^{300}g_j.
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+ \]
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+
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+ Missing or failed items receive \(IF_j=RQ_j=EE_j=1\) before aggregation. This protocol is **mean of per-item GeoAgg**, not GeoAgg of the three dataset-level dimension means. The arithmetic mean of the three dimension averages may be shown separately as a diagnostic **Mean**, but it is not used for ranking.
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  ## Task Categories
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+ These names and counts are taken directly from the frozen `benchmark_meta.json` manifest.
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+
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  | Category | Count |
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+ |---|---:|
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+ | Attribute Editing | 33 |
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+ | Camera Angle Editing | 34 |
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+ | Camera Motion Editing | 33 |
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+ | Creative Edit | 33 |
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+ | Instance Editing | 33 |
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+ | Instance Motion Editing | 33 |
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+ | Quantity Editing | 34 |
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+ | Style Editing | 34 |
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+ | Visual Effect Editing | 33 |
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+ | **Total** | **300** |
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+
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+ ## Reproducibility
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+
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+ | Artifact | Frozen identifier |
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+ |---|---|
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+ | Dataset revision | [`3bf997e7eb4fa0d0c2d56cce5ddfccc1dfbda235`](https://huggingface.co/datasets/xiangbog/VEFX-Bench/tree/3bf997e7eb4fa0d0c2d56cce5ddfccc1dfbda235) |
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+ | `benchmark_meta.json` SHA-256 | `277d89f5cf23af4163fe6ee120654210f51d19c6dee2d3aa76a3248471d1f333` |
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+ | Reward model revision | [`a15a8dbe1b3eb07ee0919e8de059f170436ec9ff`](https://huggingface.co/VEFX-Reward/VEFX-Reward-4B/tree/a15a8dbe1b3eb07ee0919e8de059f170436ec9ff) |
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+ | `model.safetensors` SHA-256 | `c3c0d03f770f0a73631206821922213de75413bb517f7d7a2fd9ab1f2c38f59d` |
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+
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+ Download by immutable revision:
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+ ```python
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+ from huggingface_hub import snapshot_download
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+
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+ snapshot_download(
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+ repo_id="xiangbog/VEFX-Bench",
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+ repo_type="dataset",
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+ revision="3bf997e7eb4fa0d0c2d56cce5ddfccc1dfbda235",
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+ local_dir="./vefx_bench_v1",
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+ )
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+ ```
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  ## Citation
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