Polish dataset card and switch dataset license to CC BY-NC 4.0

README.md

@@ -1,5 +1,5 @@
 ---
-license: mit
+license: cc-by-nc-4.0
 language:
   - ase
   - bfi
@@ -43,109 +43,171 @@ size_categories:
 pretty_name: "SignVerse-2M: A Two-Million-Clip Pose-Native Universe of 25+ Sign Languages"
 ---
 
-# SignVerse-2M
+<div align="center" style="position: relative; width: 100%; max-width: 1400px; margin: 0 auto 24px auto;">
+  <img
+    src="https://signerx.github.io/SignVerse-2M/static/images/background_gallery_big.gif"
+    alt="SignVerse-2M cover"
+    style="width: 100%; display: block; border-radius: 18px;"
+  />
+  <div
+    style="
+      position: absolute;
+      inset: 0;
+      display: flex;
+      align-items: center;
+      justify-content: center;
+      background: linear-gradient(to bottom, rgba(0,0,0,0.18), rgba(0,0,0,0.28));
+      border-radius: 18px;
+    "
+  >
+    <div
+      style="
+        color: white;
+        font-size: 3.4rem;
+        font-weight: 800;
+        letter-spacing: 0.04em;
+        text-shadow: 0 4px 20px rgba(0,0,0,0.45);
+      "
+    >
+      SignVerse-2M
+    </div>
+  </div>
+</div>
+
+## SignVerse-2M: A Two-Million-Clip Pose-Native Universe of 25+ Sign Languages
+
+Links: [[Paper]]() | [[Dataset]](https://huggingface.co/datasets/SignerX/SignVerse-2M/tree/main/dataset) | [[Project Page]](https://signerx.github.io/SignVerse-2M)
+
+**SignVerse-2M** is a large-scale multilingual pose-native dataset for sign language research. The dataset reorganizes publicly available sign language videos into a unified DWPose-based representation and releases the result as approximately **2 million clips** from **39,196 videos** covering **25+ sign languages**. Rather than distributing raw RGB video, SignVerse-2M provides per-frame body, hand, and face keypoints together with structured subtitle supervision, making the corpus directly usable for pose-conditioned sign language generation, recognition, and translation research.
+
+## Overview
+
+Existing large-scale sign language resources are typically organized as video-text corpora. That format is appropriate for RGB-based recognition or translation, but it is not the most natural interface for modern pose-driven generation pipelines, which increasingly operate on standardized human keypoint controls such as DWPose. SignVerse-2M addresses this mismatch by converting multilingual public sign language videos into a common pose space.
+
+The release is intended to support research questions such as:
+
+- multilingual sign language generation in pose space
+- pose-based sign language recognition and translation
+- cross-lingual transfer across heterogeneous sign language sources
+- benchmarking of sign language motion representations under open-world conditions
+
+## Key Characteristics
+
+| Property | Value |
+| --- | --- |
+| Dataset name | SignVerse-2M |
+| Core representation | DWPose keypoint sequences |
+| Videos | 39,196 |
+| Clips / subtitle segments | Approximately 2 million |
+| Sign languages | 25+ |
+| Frame rate | 24 FPS |
+| Per-frame keypoints | 18 body + 21 left hand + 21 right hand + 68 face = 128 |
+| Source type | Public multilingual sign language videos |
+| Raw RGB frames released | No |
+| Released supervision | Structured subtitle text and document-level text |
 
-**SignVerse-2M** is a large-scale multilingual pose-native dataset for sign language research.
-It converts ~2 million publicly available sign language video clips covering **25+ sign languages** into unified [DWPose](https://github.com/IDEA-Research/DWPose) keypoint sequences, providing a standardized interface directly compatible with modern pose-driven generation and recognition pipelines.
+## Why A Pose-Native Release
 
-> **NeurIPS 2026 Evaluations & Datasets Track submission.**  
-> [Project Page](https://signerx.github.io/SignVerse-2M) · [GitHub / Benchmark](https://github.com/SignerX/SignVerse-2M)
+SignVerse-2M should not be understood as merely a larger multilingual video-text corpus. Its main contribution is the release of a unified pose-native interface for sign language research.
 
----
+Compared with raw-video releases, the pose-native representation offers three practical advantages:
 
-## Why pose-native?
+1. It reduces nuisance variation from background, clothing, and appearance, allowing models to focus more directly on motion.
+2. It aligns naturally with contemporary pose-conditioned generation pipelines that already consume DWPose-like controls.
+3. It provides a common representation for multilingual benchmarking, making comparisons across methods more interpretable.
 
-Existing large-scale sign language datasets are built around video–text alignment for recognition and translation tasks. Over the past three years, the broader vision community has converged on DWPose as a de-facto control interface for pose-driven image/video generation (ControlNet, MimicMotion, Wan, etc.). Sign language research still lacked a resource that plugs directly into this paradigm.
+## Data Source And Processing
 
-SignVerse-2M fills that gap:
+The corpus is built from publicly available multilingual sign language videos, including resources inherited from large public sign language collections such as YouTube-SL-25 and related open web sources. Each video is processed through a unified pipeline that:
 
-| Property | SignVerse-2M | Typical video-text SL corpus |
-|---|---|---|
-| Representation | DWPose keypoints (body + hands + face) | Raw RGB video |
-| Scale | ~2 M clips, 39 K videos | Hundreds to tens of thousands of clips |
-| Languages | 25+ | 1–3 |
-| Compat. w/ modern generation | Direct | Requires re-processing |
-| Style-agnostic | Background / clothing removed | Mixed in |
+1. retrieves metadata and available subtitles,
+2. structures subtitle tracks into segment-level and document-level text,
+3. decodes the video at 24 FPS,
+4. applies DWPose to extract body, hand, and face keypoints frame by frame,
+5. packages the outputs into per-video artifacts for public release.
 
----
+No manual keypoint annotation is provided. The keypoints and subtitles are produced automatically through the preprocessing pipeline.
 
-## Dataset at a glance
+## Languages
 
-| Stat | Value |
-|---|---|
-| Videos | ~39,000 |
-| Clips / segments | ~2,000,000 |
-| Sign languages | 25+ |
-| Pose backend | DWPose (RTMPose-based) |
-| Keypoints per frame | 18 body + 21×2 hands + 68 face = 128 total |
-| Frame rate | 24 FPS |
-| Annotation | Automatic (no manual keypoint labeling) |
-| Supervision signal | Auto-structured subtitles (segment-level + document-level) |
-| Release format | Per-video `.tar` shards containing `poses.npz` + caption JSON |
-
----
+The corpus covers more than 25 sign languages. Major language codes in the current release include:
 
-## Languages covered
+| Code | Language | Code | Language |
+| --- | --- | --- | --- |
+| `ase` | American Sign Language | `lsf` | French Sign Language |
+| `bfi` | British Sign Language | `lse` | Spanish Sign Language |
+| `gsg` | German Sign Language | `lis` | Italian Sign Language |
+| `sgd` | Swiss German Sign Language | `lgp` | Portuguese Sign Language |
+| `asf` | Australian Sign Language | `ngt` | Sign Language of the Netherlands |
+| `jsl` | Japanese Sign Language | `kvk` | Korean Sign Language |
+| `csl` | Chinese Sign Language | `bzs` | Brazilian Sign Language |
+| `lsm` | Mexican Sign Language | `pjm` | Polish Sign Language |
+
+The language distribution is long-tailed rather than balanced. High-resource languages account for a disproportionate share of the total data volume.
+
+## Repository Structure
+
+The public release is organized around `.tar` shards stored under `dataset/`. Each shard contains per-video directories:
+
+```text
+dataset/
+  Sign_DWPose_NPZ_000001.tar
+  Sign_DWPose_NPZ_000002.tar
+  ...
+```
 
-The corpus inherits the language distribution of YouTube-SL-25 and other public multilingual sign language video sources. The table below lists the major languages; a long tail of additional languages is also present.
+Within each shard:
 
-| Code | Language | Code | Language |
-|---|---|---|---|
-| `ase` | American Sign Language (ASL) | `lsf` | French Sign Language (LSF) |
-| `bfi` | British Sign Language (BSL) | `lse` | Spanish Sign Language (LSE) |
-| `gsg` | German Sign Language (DGS) | `lis` | Italian Sign Language (LIS) |
-| `sgd` | Swiss German Sign Language (DSGS) | `lgp` | Portuguese Sign Language (LGP) |
-| `asf` | Australian Sign Language (Auslan) | `ngt` | Sign Language of the Netherlands (NGT) |
-| `jsl` | Japanese Sign Language (JSL) | `kvk` | Korean Sign Language (KSL) |
-| `csl` | Chinese Sign Language (CSL) | `bzs` | Brazilian Sign Language (Libras) |
-| `lsm` | Mexican Sign Language (LSM) | `pjm` | Polish Sign Language (PJM) |
+```text
+{video_id}/
+  poses.npz
+  caption.json
+  {video_id}.complete
+```
 
----
+The main files are:
 
-## Data format
+- `poses.npz`: per-video DWPose payload with frame-wise keypoints
+- `caption.json`: structured subtitle and supervision metadata
+- `.complete`: completion marker produced by the processing pipeline
 
-Each video is packaged into numbered `.tar` shards with a per-video directory structure:
+## Data Schema
 
-```
-Sign_DWPose_NPZ_XXXXXX.tar
-└── {video_id}/
-    ├── poses.npz          # DWPose keypoints for all frames
-    ├── caption.json       # Structured subtitles + English supervision
-    └── {video_id}.complete
-```
+### `poses.npz`
 
-### `poses.npz` schema
+Each `poses.npz` file stores a person-centric per-frame representation. A simplified schema is shown below:
 
 ```python
-poses.npz = {
-    "video_id":    str,          # YouTube video ID
-    "fps":         float,        # sampling rate (24.0)
-    "num_frames":  int,
-    "frame_ids":   int[T],       # 0-indexed frame indices
-    "width":       int,
-    "height":      int,
-    "frames": [                  # list of T per-frame payloads
+{
+    "video_id": str,
+    "fps": float,
+    "num_frames": int,
+    "frame_ids": int[T],
+    "width": int,
+    "height": int,
+    "frames": [
         {
             "num_people": int,
-            "frame_id":   int,
-            "width":      int,
-            "height":     int,
+            "frame_id": int,
+            "width": int,
+            "height": int,
             "person_0": {
-                "body":       float[18, 3],   # (x, y, score)
-                "face":       float[68, 3],
-                "left_hand":  float[21, 3],
+                "body": float[18, 3],
+                "face": float[68, 3],
+                "left_hand": float[21, 3],
                 "right_hand": float[21, 3],
             },
-            # "person_1": { ... },  # if multiple signers detected
+            # optional additional people:
+            # "person_1": { ... }
         },
         ...
     ]
 }
 ```
 
-Keypoint coordinates are in **pixel space** (not normalized). Confidence scores are in `[0, 1]`.
+Keypoint coordinates are stored in pixel space as `(x, y, score)`, where confidence scores lie in `[0, 1]`.
 
-### `caption.json` schema
+### `caption.json`
 
 ```json
 {
@@ -154,40 +216,41 @@ Keypoint coordinates are in **pixel space** (not normalized). Confidence scores
   "title": "...",
   "duration_s": 312.4,
   "segments": [
-    {"start": 0.0, "end": 4.2, "text": "Hello, welcome to ..."},
-    ...
+    { "start": 0.0, "end": 4.2, "text": "..." }
   ],
-  "document_text": "Hello, welcome to ...",
+  "document_text": "...",
   "english_source": "native"
 }
 ```
 
-`english_source` is either `"native"` or `"translated_from:<lang>"`, indicating whether the English supervision came from a native English track or an auto-selected translation.
-
----
-
-## Quick start
+The field `english_source` records whether the English supervision is native or automatically selected from an available translated subtitle track.
 
-### Load a single shard
+## Loading Example
 
 ```python
-import tarfile, numpy as np, json
+import json
+import tarfile
+import numpy as np
 
-with tarfile.open("Sign_DWPose_NPZ_000001.tar") as tar:
-    tar.extractall("./extracted/")
+with tarfile.open("dataset/Sign_DWPose_NPZ_000001.tar") as tar:
+    tar.extractall("./tmp_signverse")
 
-# Read poses for a video
-npz = np.load("extracted/{video_id}/poses.npz", allow_pickle=True)
-frames = npz["frames"].tolist()           # list of per-frame dicts
-body_kps = frames[0]["person_0"]["body"]  # shape (18, 3) → (x, y, score)
+npz = np.load("./tmp_signverse/{video_id}/poses.npz", allow_pickle=True)
+frames = npz["frames"].tolist()
+body = frames[0]["person_0"]["body"]
 
-# Read caption
-with open("extracted/{video_id}/caption.json") as f:
+with open("./tmp_signverse/{video_id}/caption.json", "r", encoding="utf-8") as f:
     caption = json.load(f)
+
+print(body.shape)
 print(caption["segments"][0]["text"])
 ```
 
-### Visualize poses
+## Visualization And Reproduction
+
+The repository includes scripts for inspecting the released pose files and for reproducing the processing pipeline.
+
+### Visualize one pose file
 
 ```bash
 python scripts/visualize_dwpose_npz.py \
@@ -196,7 +259,7 @@ python scripts/visualize_dwpose_npz.py \
     --out viz/
 ```
 
-### Reproduce the full pipeline
+### Reproduce the pipeline
 
 ```bash
 # Single machine
@@ -206,59 +269,52 @@ bash reproduce_independently.sh
 bash reproduce_independently_slurm.sh
 ```
 
-See `scripts/` for the three pipeline stages: (1) video download + caption structuring, (2) DWPose extraction, (3) upload to HuggingFace.
+The pipeline is organized into acquisition, subtitle structuring, pose extraction, and upload/publication stages.
 
----
+## Benchmark Setting
 
-## Benchmark tasks and baseline
+The accompanying paper introduces a multilingual **text-to-pose** benchmark for sign language generation. A generated DWPose sequence is evaluated through back-translation into spoken text, and standard text metrics such as BLEU and ROUGE are reported against the source input. The benchmark repository also provides a **SignDW Transformer** baseline in both small and large model configurations.
 
-The paper defines a **text-to-pose** sign language generation task evaluated via back-translation:
-
-> generated DWPose sequence → pose-space SL translator → spoken text → BLEU / ROUGE vs. original input
-
-A **SignDW Transformer** baseline (40M and 1.2B parameter variants) is provided. Benchmark code and model weights are at [github.com/SignerX/SignVerse-2M](https://github.com/SignerX/SignVerse-2M).
-
----
+For model code and experimental setup, refer to the benchmark repository:
 
-## Limitations
+- [https://github.com/SignerX/SignVerse-2M](https://github.com/SignerX/SignVerse-2M)
 
-- **Automatic pose extraction.** DWPose can produce missing or jittery keypoints on challenging frames (fast motion, partial occlusion, multiple signers).
-- **Fine-grained hand detail.** The 21-keypoint hand model does not fully capture handshape distinctions needed for lexical discrimination.
-- **Non-manual features.** Facial expressions and mouth patterns carry linguistic meaning in many sign languages; the 68-point face model is a partial proxy.
-- **Language imbalance.** The corpus follows a long-tail distribution; ASL and a few other languages dominate total hours.
-- **Subtitle quality.** Captions are automatically structured from platform exports; mistranslations and alignment errors propagate into the supervision signal.
-- **Primary-signer assumption.** The pipeline indexes `person_0` as the primary signer; multi-signer frames may be misattributed.
-- **No manual annotation.** No human-verified keypoints or signer identity metadata are included.
+## Intended Use
 
----
+The release is intended for research use, including:
 
-## Intended use
+- sign language generation from text via pose space
+- pose-based sign language translation and recognition
+- cross-lingual transfer, adaptation, and benchmarking
+- comparison of pose-native motion representations under open-world distributions
 
-**In scope:**
-- Multilingual sign language generation (text → pose → video)
-- Pose-space sign language recognition and translation research
-- Cross-lingual transfer and adaptation studies
-- Compatibility and benchmarking with modern pose-driven video generation models
+The release is not intended for:
 
-**Out of scope:**
-- Safety-critical deployment (medical or legal sign language interpretation) without additional validation
-- Re-identification of individual signers
-- Definitive linguistic completeness claims for any specific sign language
+- safety-critical interpretation in medical, legal, or emergency settings
+- re-identification of individual signers
+- claims of full linguistic coverage for any specific sign language
 
----
+## Limitations
 
-## Responsible AI
+Users should account for the following limitations when interpreting results:
 
-**Personal information.** The corpus is derived from publicly posted YouTube videos. Only DWPose keypoint sequences and structured subtitle text are released; raw RGB frames are **not** distributed. Pose sequences may nonetheless allow re-identification of signers when combined with metadata.
+- Keypoints are extracted automatically and may be noisy under fast motion, occlusion, or multi-person scenes.
+- Fine-grained handshape distinctions are only partially captured by the released 21-keypoint hand representation.
+- Non-manual linguistic signals such as facial expression and mouthing are only partially represented by 68 face landmarks.
+- Subtitle timing and translations are automatically processed and may contain alignment or semantic errors.
+- The corpus is language-imbalanced and inherits the long-tail distribution of public web video sources.
+- `person_0` is treated as the primary signer, which may be imperfect in multi-signer videos.
 
-**Bias.** Content skews toward online educational and interpreter videos; spontaneous or conversational signing is underrepresented. High-resource languages (ASL, BSL) dominate total hours.
+## Responsible Use
 
-**Consent.** Videos were publicly posted under terms that permit academic reuse. No raw video is redistributed.
+SignVerse-2M is derived from publicly posted sign language videos. This repository does **not** redistribute raw RGB videos; it releases pose keypoints and structured subtitle text only. Even so, pose sequences may still carry information that can contribute to signer identification when combined with external metadata. Users should treat the corpus as human-subject-derived data and use it responsibly.
 
----
+The data distribution is also shaped by what is publicly available online. Educational or interpreter-style content may be overrepresented, while conversational, regional, or community-specific signing practices may be underrepresented.
 
 ## Citation
 
+If you use SignVerse-2M in academic work, please cite:
+
 ```bibtex
 @inproceedings{fang2026signverse2m,
   title     = {{SignVerse-2M}: A Two-Million-Clip Pose-Native Universe of 25+ Sign Languages},
@@ -269,9 +325,8 @@ A **SignDW Transformer** baseline (40M and 1.2B parameter variants) is provided.
 }
 ```
 
----
-
 ## License
 
-Released under the [MIT License](LICENSE).  
-The underlying video content remains subject to YouTube's Terms of Service and the respective creators' rights. Only pose keypoints and structured caption text are distributed.
+The released dataset annotations, pose keypoints, and accompanying metadata are distributed under **CC BY-NC 4.0**.
+
+Source videos are **not** redistributed in this repository and remain subject to the original platform terms and the rights of their respective creators.

metadata.json

@@ -40,7 +40,7 @@
   "conformsTo": "http://mlcommons.org/croissant/1.0",
   "description": "SignVerse-2M is a large-scale multilingual pose-native dataset for sign language research. It converts approximately two million publicly available sign language video clips covering 25+ sign languages into unified DWPose keypoint sequences (18 body + 21x2 hand + 68 face keypoints per frame at 24 FPS), providing a standardized data interface directly compatible with modern pose-driven generation and recognition pipelines.",
   "citeAs": "@inproceedings{fang2026signverse2m, title={{SignVerse-2M}: A Two-Million-Clip Pose-Native Universe of 25+ Sign Languages}, author={Fang, Sen and Zhong, Hongbin and Zhang, Yanxin and Metaxas, Dimitris N.}, booktitle={Advances in Neural Information Processing Systems (NeurIPS)}, year={2026}, note={Evaluations \\& Datasets Track}}",
-  "license": "https://opensource.org/licenses/MIT",
+  "license": "https://creativecommons.org/licenses/by-nc/4.0/",
   "url": "https://huggingface.co/datasets/SignerX/SignVerse-2M",
   "version": "1.0.0",
   "keywords": [