Update README.md

README.md

@@ -249,4 +249,278 @@ language:
 - en
 size_categories:
 - 1K<n<10K
----
+---
+# VP-Bench
+**VP-Bench** is the official evaluation benchmark for [**FlowInOne: Unifying Multimodal Generation as Image-in, Image-out Flow Matching**](https://csu-jpg.github.io/FlowInOne.github.io/).
+It is a rigorously curated benchmark assessing **instruction faithfulness**, **spatial precision**, **visual realism**, and **content consistency** across eight distinct visual prompting tasks.
+> 📄 **Paper**: FlowInOne: Unifying Multimodal Generation as Image-in, Image-out Flow Matching  
+> 🌐 **Project Page**: https://csu-jpg.github.io/FlowInOne.github.io/  
+> 💻 **Code & Evaluation Scripts**: https://github.com/CSU-JPG/FlowInOne
+## Evaluation
+Our evaluation scripts are now available on [GitHub](https://github.com/CSU-JPG/FlowInOne)!
+## Dataset Subsets
+The dataset contains **8 subsets**, each corresponding to a distinct visual instruction task:
+| Subset | Abbrev. | Description |
+|--------|---------|-------------|
+| `class2image` | C2I | Class label rendered in input image → generate corresponding image |
+| `text2image` | T2I | Text instruction rendered in input image → generate image |
+| `text_in_image` | TIE | Edit text content within an image |
+| `force` | FU | Physics-aware force understanding (3 categories) |
+| `text_box_control` | TBE | Text and bounding box editing |
+| `trajectory` | TU | Trajectory understanding and prediction |
+| `vismarker` | VME | Visual marker guided editing (8 categories) |
+| `doodles` | DE | Doodle-based editing |
+## Dataset Features
+- **input_image** (`image`): The input visual prompt image (with rendered instruction).
+- **output_image** (`image`): The ground-truth output image.
+- **recognized_text** (`string`): The text instruction rendered in the input image (extracted via OCR annotation).
+- **subset** (`string`): The subset name.
+- **category** (`string`): Sub-category within a subset (empty string if not applicable).
+- **image_name** (`string`): The image filename.
+- **input_relpath** (`string`): Relative path of the input image within the subset.
+- **output_relpath** (`string`): Relative path of the output image within the subset.
+- **pair_id** (`string`): Stable SHA1 identifier for each input-output pair.
+## Loading the Dataset
+### class2image
+```python
+from datasets import load_dataset
+ds = load_dataset("CSU-JPG/VP-Bench", "class2image", split="train")
+text2image
+from datasets import load_dataset
+ds = load_dataset("CSU-JPG/VP-Bench", "text2image", split="train")
+text_in_image
+from datasets import load_dataset
+ds = load_dataset("CSU-JPG/VP-Bench", "text_in_image", split="train")
+force
+from datasets import load_dataset
+ds = load_dataset("CSU-JPG/VP-Bench", "force", split="train")
+text_box_control
+from datasets import load_dataset
+ds = load_dataset("CSU-JPG/VP-Bench", "text_box_control", split="train")
+trajectory
+from datasets import load_dataset
+ds = load_dataset("CSU-JPG/VP-Bench", "trajectory", split="train")
+vismarker
+from datasets import load_dataset
+ds = load_dataset("CSU-JPG/VP-Bench", "vismarker", split="train")
+doodles
+from datasets import load_dataset
+ds = load_dataset("CSU-JPG/VP-Bench", "doodles", split="train")
+Load All Subsets
+from datasets import load_dataset, concatenate_datasets
+subsets = ["class2image", "text2image", "text_in_image", "force",
+           "text_box_control", "trajectory", "vismarker", "doodles"]
+ds_all = concatenate_datasets([
+    load_dataset("CSU-JPG/VP-Bench", name=s, split="train") for s in subsets
+])
+Evaluation Results
+We evaluate multiple methods on VP-Bench using three state-of-the-art VLM evaluators (Gemini3, GPT-5.2, Qwen3.5) and human judges. The metric is success ratio (higher is better). Total denotes the average success rate across all eight task categories.
+
+Abbreviations: C2I: class-to-image · T2I: text-to-image · TIE: text-in-image edit · FU: force understanding · TBE: text & bbox edit · TU: trajectory understanding · VME: visual marker edit · DE: doodles edit
+
+Evaluator: Gemini3
+Method	C2I	T2I	TIE	FU	TBE	TU	VME	DE	Total
+Nano Banana (Google, 2025)
+.650
+.980
+.423
+.520
+.614
+.020
+.548
+.721
+.560
+Omnigen2 (Wu et al., 2025)
+.020
+.020
+.017
+.020
+.000
+.000
+.000
+.000
+.007
+Kontext (Labs et al., 2025)
+.050
+.020
+.048
+.007
+.000
+.020
+.010
+.000
+.019
+Qwen-IE-2509 (Wu et al., 2025)
+.230
+.040
+.069
+.000
+.000
+.020
+.023
+.000
+.048
+FlowInOne (Ours)
+.890
+.700
+.355
+.727
+.302
+.520
+.292
+.535
+.540
+Evaluator: GPT-5.2
+Method	C2I	T2I	TIE	FU	TBE	TU	VME	DE	Total
+Nano Banana (Google, 2025)
+.680
+.959
+.152
+.127
+.040
+.136
+.302
+—
+.302
+Omnigen2 (Wu et al., 2025)
+.110
+.020
+.000
+.000
+.000
+.000
+.000
+.023
+.019
+Kontext (Labs et al., 2025)
+.090
+.020
+.028
+.020
+.000
+.080
+.003
+.093
+.042
+Qwen-IE-2509 (Wu et al., 2025)
+.240
+.120
+.080
+.020
+.022
+.060
+.020
+.047
+.076
+FlowInOne (Ours)
+.850
+.800
+.079
+.500
+.116
+.240
+.083
+.465
+.392
+Evaluator: Qwen3.5
+Method	C2I	T2I	TIE	FU	TBE	TU	VME	DE	Total
+Nano Banana (Google, 2025)
+.600
+.959
+.386
+.367
+.257
+.040
+.321
+.744
+.469
+Omnigen2 (Wu et al., 2025)
+.030
+.020
+.017
+.034
+.000
+.000
+.003
+.047
+.019
+Kontext (Labs et al., 2025)
+.050
+.020
+.042
+.133
+.000
+.060
+.047
+.093
+.056
+Qwen-IE-2509 (Wu et al., 2025)
+.270
+.060
+.080
+.087
+.047
+.040
+.033
+.047
+.083
+FlowInOne (Ours)
+.859
+.720
+.354
+.713
+.272
+.320
+.306
+.481
+.503
+Evaluator: Human
+Method	C2I	T2I	TIE	FU	TBE	TU	VME	DE	Total
+Nano Banana (Google, 2025)
+.602
+.904
+.271
+.250
+.200
+.050
+.229
+.742
+.406
+Omnigen2 (Wu et al., 2025)
+.000
+.000
+.000
+.000
+.000
+.000
+.000
+.000
+.000
+Kontext (Labs et al., 2025)
+.000
+.000
+.043
+.000
+.000
+.000
+.000
+.100
+.018
+Qwen-IE-2509 (Wu et al., 2025)
+.067
+.000
+.029
+.000
+.000
+.000
+.000
+.000
+.012
+FlowInOne (Ours)
+.800
+.645
+.242
+.705
+.255
+.280
+.255