Datasets:

AIPeanutman
/

OmitI2V

license: apache-2.0
language:
  - en
pretty_name: OmitI2V
size_categories:
  - n<1K
task_categories:
  - image-to-video
tags:
  - text-to-video
  - image-to-video
  - text-guided-image-to-video
  - benchmark
  - prompt-adherence
  - semantic-fidelity
  - video-generation

OmitI2V

OmitI2V is a benchmark for evaluating semantic fidelity (prompt adherence) in text-guided image-to-video (TI2V) generation, focusing on prompts that require substantial edits to the reference image — object addition, deletion, and modification. It is the benchmark introduced in AlignVid: Taming Visual Dominance via Training-Free Attention Modulation in Text-guided Image-to-Video Generation (ICML 2026).


📄 Paper	https://arxiv.org/abs/2512.01334
💻 Code	https://github.com/LAW1223/AlignVid
🌐 Project page	https://law1223.github.io/AlignVid/

It contains 367 human-annotated samples. Each sample pairs a reference image with an editing instruction and a set of yes/no VQA questions that probe whether the requested edit is actually realized in the generated video.

Dataset structure

.
├── meta.json        # 367 annotated samples
├── modification/    # reference images, organized by sub-category / domain
├── addition/
└── deletion/

Images are referenced by the image-path field in meta.json, relative to the dataset root (e.g., modification/pose/human/sample_0.jpg).

Fields (per entry in `meta.json`)

field	type	description
`id`	str	unique sample id (e.g., `sample_0`)
`image-path`	str	reference image path, relative to the dataset root
`prompt`	str	the text instruction driving the video
`main-category`	str	one of `modification`, `addition`, `deletion`
`sub-category`	str	finer edit type (e.g., `pose`, `appearance`, `element`)
`domain`	str	content domain (e.g., `human`, `animal`, `nature`, `building`)
`type`	str	image source: `real image`, `generated image`, or `animation image`
`key`	list[str]	short keyword(s) summarizing the target change
`expected-change`	str	natural-language description of the expected edit
`resolution`	str	image resolution
`aspect_ratio`	str	image aspect ratio
`questions`	list[dict]	VQA items, each `{id, question, expected_answer, category}`

Statistics

Samples: 367
Main category: modification 113 · addition 129 · deletion 125
Image type: real image 290 · animation image 56 · generated image 21
Domains: human, animal, nature, object, building, animation, environment, effect, and more.

Usage

import json

meta = json.load(open("meta.json", encoding="utf-8"))
print(len(meta))                 # 367
ex = meta[0]
print(ex["prompt"])              # editing instruction
print(ex["image-path"])          # e.g. modification/pose/human/sample_0.jpg
for q in ex["questions"]:
    print(q["question"], "->", q["expected_answer"])

The questions provide a yes/no protocol for measuring fine-grained edit compliance of a generated video against the prompt.

Evaluation

The full evaluation pipeline (VQA-based semantic fidelity with Qwen2.5-VL, plus VBench-style visual-quality metrics) and ready-to-edit inference scripts for FramePack, FramePack-F1, and Wan2.1 live in the AlignVid repository:

https://github.com/LAW1223/AlignVid

License

Released under the Apache-2.0 License.

Citation

@article{liu2025alignvid,
  title={AlignVid: Training-Free Attention Scaling for Semantic Fidelity in Text-Guided Image-to-Video Generation},
  author={Liu, Yexin and Shu, Wen-Jie and Huang, Zile and Zheng, Haoze and Wang, Yueze and Zhang, Manyuan and Lim, Ser-Nam and Yang, Harry},
  journal={arXiv preprint arXiv:2512.01334},
  year={2025}
}