--- pretty_name: TerraVis Annotations license: other license_name: terravis-annotations-mixed license_link: https://huggingface.co/datasets/ShyFoo/TerraVis-Annotations/blob/main/LICENSE language: - en size_categories: - 1K **The prompts were used only to generate the images.** Annotators rated each image without seeing its prompt, so the ratings describe the world consistency of the image itself. The `prompt` field is released for completeness, to document how each image was produced. | `model` | Model | Access | |------------------------------|-----------------------------------------------------------------------------------------------------|--------------| | `gpt-image-1.5` | GPT Image 1.5 | OpenAI API | | `gemini-3-pro-image-preview` | Nano Banana Pro | Gemini API | | `flux.2-dev` | [FLUX.2 [dev]](https://huggingface.co/black-forest-labs/FLUX.2-dev) | open weights | | `qwen-image-2512` | [Qwen-Image-2512](https://huggingface.co/Qwen/Qwen-Image-2512) | open weights | | `stable-diffusion-3.5-large` | [Stable Diffusion 3.5 Large](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) | open weights | ## Quick start Requires `datasets>=4.0`. The full download is about 7 GB; pass `split="coco_t2i"` or `streaming=True` for a first look. ```python from datasets import load_dataset ds = load_dataset("ShyFoo/TerraVis-Annotations") # splits: coco_t2i, genai_bench row = ds["genai_bench"][0] row["image"] # PIL.Image, 1024x1024 row["ratings"] # Worker 1-3 ratings, e.g. [2, 1, 2]; None means N/A row["rating_mean"] # human score used in the paper; None if out of scope # Mean human score per model (dropping the image column avoids decoding every image) df = ds["genai_bench"].remove_columns("image").to_pandas().dropna(subset=["rating_mean"]) print(df.groupby("model")["rating_mean"].mean()) # Original file bytes, e.g. a Nano Banana Pro JPEG that still carries C2PA from datasets import Image raw = ds["coco_t2i"].cast_column("image", Image(decode=False))[1]["image"] raw["path"] # 'coco_t2i-gemini-3-pro-image-preview-seed=1111-001.jpg' ``` ## Data fields | Field | Type | Description | |--------------------------|------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `image` | image | The generated image, 1024ร—1024, stored as the file written by the generation pipeline and not re-encoded for this release (PNG, or JPEG for 437 Nano Banana Pro images; see [Generation](#generation)). | | `image_id` | string | `{split}-{model}-seed=1111-{prompt_id}`, e.g. `coco_t2i-flux.2-dev-seed=1111-001`; the identifier used in the TerraVis code and result files. | | `benchmark` | string | `COCO-T2I` or `GenAI-Bench`. | | `prompt_id` | string | 0-based, zero-padded index into the benchmark's prompt list in the TerraVis repository (`000`โ€“`199` for COCO-T2I, `0000`โ€“`1599` for GenAI-Bench; GenAI-Bench ids equal the upstream ids). The five images of a prompt share it. | | `prompt` | string | The prompt sent to this model. | | `model` | string | Generating model, see the table above. | | `ratings` | list[int8] | The three 1โ€“5 world-consistency ratings, Worker 1, 2 and 3 in that order. `None` means N/A. | | `has_violation` | list[bool] | Worker 1โ€“3's answers to "Does this image have a world-consistency violation?", in the same order as `ratings`. `None` means N/A. | | `rating_mean` | float64 | Mean of the non-N/A values in `ratings`; the human score used in the paper. `None` when two or more are N/A (the image is out of scope). | | `has_violation_majority` | bool | `True` if at least half of the non-N/A `has_violation` answers say there is a violation; the label the paper uses. `None` means N/A. | In every collected answer, "no violation" came with rating 5 and "violation" with a rating of 1โ€“4, so `has_violation` equals `rating < 5` wherever both are present. Rows are ordered by `prompt_id`, then by model in the order of the table above. Example row (image omitted): ```json { "image_id": "genai_bench-gpt-image-1.5-seed=1111-0253", "benchmark": "GenAI-Bench", "prompt_id": "0253", "prompt": "In a modern laboratory, all the computer screens are turned on.", "model": "gpt-image-1.5", "ratings": [4, 5, 3], "has_violation": [true, false, true], "rating_mean": 4.0, "has_violation_majority": true } ``` ## Annotation Ratings were collected on Amazon Mechanical Turk, one image per task. Annotators saw the image only; the prompt and the model were not shown. Before rating, they read the TerraVis taxonomy of object-, interaction- and scene-level world-consistency violations. They then answered a binary question (violation: yes / no / N/A) and gave a rating: - **5**: Fully consistent (no visible world-consistency violation). - **4**: Mostly consistent (one minor violation). - **3**: Partially consistent (multiple minor violations). - **2**: Weakly consistent (one major violation, with or without an additional minor violation). - **1**: Completely inconsistent (multiple major violations, or one major violation with multiple minor violations). - **N/A**: The image does not depict a representational object or scene (for example abstract patterns, logos, charts, UI mock-ups, posters or microscopic textures). Stylised images (cartoon, painterly and so on) were in scope, and annotators were told not to penalise their intentionally reduced photorealism. ## Prompts - **COCO-T2I**: 100 of the 200 COCO-T2I prompts in (Yarom et al., NeurIPS 2023). - **GenAI-Bench**: 800 of the 1,600 GenAI-Bench prompts (Li et al., 2024). The 900 prompts are the human-annotated subset used in the paper, a random 50% of each benchmark. The `prompt` field holds the text each model received. The full list of modifications is in [LICENSE](https://huggingface.co/datasets/ShyFoo/TerraVis-Annotations/blob/main/LICENSE). ## Generation - **Dates and resolution**: all images are 1024ร—1024 and were generated between December 2025 and April 2026. - **Open-weight models**: run with ๐Ÿค— Diffusers, default pipeline sampling settings and no safety checker. A global seed of 1111 was set once per generation run; there was no per-image generator, so individual images may not be reproducible in isolation. - **API models**: called through their official APIs at the 1024ร—1024 square size, with no seed. The OpenAI Images API has no seed parameter, and the optional Gemini seed was left unset. - **File formats**: PNG, or JPEG. ## Licensing | Part | License | |-----------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Annotations and metadata | CC BY 4.0 | | Images from `gpt-image-1.5`, `gemini-3-pro-image-preview`, `qwen-image-2512` and `stable-diffusion-3.5-large` (3,600) | CC BY 4.0 | | Images from `flux.2-dev` (900) | Non-commercial terms required by the FLUX.2 [dev] licence ([LICENSE](https://huggingface.co/datasets/ShyFoo/TerraVis-Annotations/blob/main/LICENSE) ยง2b); not a Creative Commons licence | | COCO-T2I prompts | CC BY 4.0 (COCO Consortium captions, selected by SeeTRUE) | | GenAI-Bench prompts | Apache 2.0 | - **flux.2-dev images** may not be used for commercial or production purposes, for military, surveillance or biometric purposes, or in the other ways listed in LICENSE ยง2b. These restrictions come from section 4(a) of the FLUX.2 [dev] licence. Anyone who redistributes these images must pass the same terms on. - **Other images.** OpenAI, Google, Alibaba Cloud (Qwen) and Stability AI claim no ownership of these outputs and do not require a non-commercial licence. As far as the authors can tell, their terms place no conditions on people who receive the images; they bind the authors, for example by restricting the authors' use of the outputs to develop competing or foundational generative models. - **Requests, not conditions** (for the flux.2-dev images, some of these are already conditions under LICENSE ยง2b): please do not use the images to train image-generation models, do not present them as human-made or as real photographs, and keep the C2PA credentials in the Nano Banana Pro JPEGs. - **Prompts** keep their original licences. None of the restrictions or requests above applies to them. - The image licences cover only whatever rights the authors hold in AI-generated images. The dataset is published by the TerraVis authors (see [Citation](#citation)). None of the model providers, Amazon or the upstream dataset authors is affiliated with or endorses this dataset. See [LICENSE](https://huggingface.co/datasets/ShyFoo/TerraVis-Annotations/blob/main/LICENSE) for details, including the versions of the terms in force when the images were generated. ## Citation ```bibtex @article{fu2026terravis, title={TerraVis: Towards Evaluation of World-Grounded Visual Consistency in Text-to-Image Generation via MLLM Workflows}, author={Fu, Shuai and Gu, Jing and Zhou, Jian and Duan, Zicheng and Zhou, Gengze and Wu, Qi}, journal={arXiv preprint arXiv:2610.02959}, year={2026} } ``` Please also cite the prompt sources: SeeTRUE ([Yarom et al., NeurIPS 2023](https://proceedings.neurips.cc/paper_files/paper/2023/hash/056e8e9c8ca9929cb6cf198952bf1dbb-Abstract-Conference.html)), MS-COCO ([Lin et al., ECCV 2014](https://doi.org/10.1007/978-3-319-10602-1_48)) and GenAI-Bench ([Li et al., 2024](https://linzhiqiu.github.io/papers/genai_bench/)).