| --- |
| license: cc-by-nc-4.0 |
| pretty_name: HIM-2K |
| task_categories: |
| - image-segmentation |
| tags: |
| - human-instance-matting |
| - alpha-matte |
| - matting |
| --- |
| |
| # HIM-2K |
|
|
| HIM-2K is a human instance matting benchmark introduced in |
| **"Human Instance Matting via Mutual Guidance and Multi-Instance Refinement"** |
| (InstMatt, Sun et al., CVPR 2022). Please cite the original authors and respect |
| the non-commercial (CC BY-NC 4.0) license. |
|
|
| ## Schema |
|
|
| One row per image: |
|
|
| - `image` — the RGB photo (`datasets.Image`). |
| - `mask` — a **list** of per-instance alpha mattes for that image |
| (`List(Image(...))`); each element is one human instance alpha, ordered by the |
| instance index in the source archive. |
| - `subset` — `"comp"` or `"natural"` (matches the split name). |
|
|
| ## Splits |
|
|
| - `comp` — 1680 composited images. |
| - `natural` — 320 natural images. |
|
|
| The `images/natural_wo_gt` portion of the original archive is **excluded** here |
| because it has no ground-truth alpha mattes. |
|
|
| ## Citation |
|
|
| ``` |
| @inproceedings{sun2022instmatt, |
| title = {Human Instance Matting via Mutual Guidance and Multi-Instance Refinement}, |
| author = {Sun, Yanan and Tang, Chi-Keung and Tai, Yu-Wing}, |
| booktitle = {CVPR}, |
| year = {2022} |
| } |
| ``` |
|
|
