{
  "id": 673073,
  "title": "Introducing Kiseki – Lightweight .npy Motion Visualization for Motion-S",
  "url": "/competitions/motion-s-hierarchical-text-to-motion-generation-for-sign-language/discussion/673073",
  "author_name": "Anthony",
  "post_date": "2026-02-12T08:42:58.841000",
  "votes": 3,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Hello Motion-S participants! 👋\nAs many of you are starting to generate tokens → decode them with <code>rvq_vae_best.pth</code> → get <code>.npy</code> motion arrays, I wanted to share a small open-source helper tool we’ve just made public to make your life easier during development and debugging.</p>\n<h3>Kiseki (軌跡) – Trajectory Visualization</h3>\n<p>Repo: <a href=\"https://github.com/signvrse/kiseki\" target=\"_blank\">https://github.com/signvrse/kiseki</a><br>\nLicense: MIT<br>\nDependencies: only <strong>numpy</strong> + <strong>matplotlib</strong> (no torch, no heavy motion libs)  </p>\n<h3>Quick Install (in your Kaggle notebook or local env)</h3>\n<pre><code>!pip install git+https://github.com/signvrse/kiseki.git\n</code></pre>\n<p>Kiseki takes your decoded <code>.npy</code> motion features (the same format you get from the VAE decoder: shape ≈ <code>[seq_len, num_joints*3]</code> or similar, root-relative or normalized) and turns them into:</p>\n<ul>\n<li>Animated skeleton videos (.mp4)</li>\n<li>Trajectory trails (see wrist / fingertip paths over time)</li>\n<li>Hand-focused or upper-body zoomed views</li>\n<li>Side-by-side or overlay comparison (great for generated vs. ground-truth)</li>\n<li>Static key-frame grid PNGs</li>\n<li>Frame-range clipping (debug only part of a long sequence)\nIt ships with a default BVH skeleton (<code>sample.bvh</code>) so you don’t need to provide one — but you can override it if your pipeline uses a different topology.</li>\n</ul>\n<h3>Usage</h3>\n<h4>To visualize a specific joint in a certain view</h4>\n<pre><code># Focus on hands with front view\nvisualize(\"motion.npy\",\n         focus_joints='both_hands',\n         fixed_view='front',\n         fps=60)\n</code></pre>\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F11979020%2F7ed71d22cdfc55626050dfc96fc6a8c3%2Fdownload%20(6).gif?generation=1770884908488554&amp;alt=media\" alt=\"\"></p>\n<h4>Compare two motion sequences (e.g. generated vs ground truth).</h4>\n<pre><code>from kiseki import compare\n# Overlay -- both skeletons on the same axes\ncompare(\"generated.npy\", \"ground_truth.npy\", mode=\"overlay\")\n# Side-by-side -- two panels\ncompare(\"generated.npy\", \"ground_truth.npy\", mode=\"side_by_side\")\n# With labels and fixed view\ncompare(\"a.npy\", \"b.npy\",\n       mode=\"side_by_side\",\n       fixed_view='front',\n       label_a=\"Generated\",\n       label_b=\"Ground Truth\")\n</code></pre>\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F11979020%2F93774fd1cb3ae6851caa085aad300cd4%2Fdownload%20(7).gif?generation=1770885205056393&amp;alt=media\" alt=\"\"></p>\n<h4>Frame Range / Clip Selection</h4>\n<p>Render only a portion of the motion.</p>\n<pre><code>visualize(\"motion.npy\", start_frame=50, end_frame=200)\n</code></pre>\n<h3>Why It Helps in Motion-S</h3>\n<ul>\n<li>Quickly check if your generated motions look <strong>physically plausible</strong> (no flying hands, reasonable speeds)</li>\n<li>Spot <strong>mode collapse</strong> or frozen joints early</li>\n<li>Compare your output vs. real KSL samples from train set</li>\n<li>Visualize <strong>fingertip trajectories</strong> — critical for sign language clarity</li>\n<li>Focus camera on <strong>hands / arms</strong> without heavy 3D rendering setup</li>\n</ul>",
  "messages": [
    {
      "id": 3405153,
      "postDate": "2026-02-12T08:42:58.843Z",
      "content": "<p>Hello Motion-S participants! 👋\nAs many of you are starting to generate tokens → decode them with <code>rvq_vae_best.pth</code> → get <code>.npy</code> motion arrays, I wanted to share a small open-source helper tool we’ve just made public to make your life easier during development and debugging.</p>\n<h3>Kiseki (軌跡) – Trajectory Visualization</h3>\n<p>Repo: <a href=\"https://github.com/signvrse/kiseki\" target=\"_blank\">https://github.com/signvrse/kiseki</a><br>\nLicense: MIT<br>\nDependencies: only <strong>numpy</strong> + <strong>matplotlib</strong> (no torch, no heavy motion libs)  </p>\n<h3>Quick Install (in your Kaggle notebook or local env)</h3>\n<pre><code>!pip install git+https://github.com/signvrse/kiseki.git\n</code></pre>\n<p>Kiseki takes your decoded <code>.npy</code> motion features (the same format you get from the VAE decoder: shape ≈ <code>[seq_len, num_joints*3]</code> or similar, root-relative or normalized) and turns them into:</p>\n<ul>\n<li>Animated skeleton videos (.mp4)</li>\n<li>Trajectory trails (see wrist / fingertip paths over time)</li>\n<li>Hand-focused or upper-body zoomed views</li>\n<li>Side-by-side or overlay comparison (great for generated vs. ground-truth)</li>\n<li>Static key-frame grid PNGs</li>\n<li>Frame-range clipping (debug only part of a long sequence)\nIt ships with a default BVH skeleton (<code>sample.bvh</code>) so you don’t need to provide one — but you can override it if your pipeline uses a different topology.</li>\n</ul>\n<h3>Usage</h3>\n<h4>To visualize a specific joint in a certain view</h4>\n<pre><code># Focus on hands with front view\nvisualize(\"motion.npy\",\n         focus_joints='both_hands',\n         fixed_view='front',\n         fps=60)\n</code></pre>\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F11979020%2F7ed71d22cdfc55626050dfc96fc6a8c3%2Fdownload%20(6).gif?generation=1770884908488554&amp;alt=media\" alt=\"\"></p>\n<h4>Compare two motion sequences (e.g. generated vs ground truth).</h4>\n<pre><code>from kiseki import compare\n# Overlay -- both skeletons on the same axes\ncompare(\"generated.npy\", \"ground_truth.npy\", mode=\"overlay\")\n# Side-by-side -- two panels\ncompare(\"generated.npy\", \"ground_truth.npy\", mode=\"side_by_side\")\n# With labels and fixed view\ncompare(\"a.npy\", \"b.npy\",\n       mode=\"side_by_side\",\n       fixed_view='front',\n       label_a=\"Generated\",\n       label_b=\"Ground Truth\")\n</code></pre>\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F11979020%2F93774fd1cb3ae6851caa085aad300cd4%2Fdownload%20(7).gif?generation=1770885205056393&amp;alt=media\" alt=\"\"></p>\n<h4>Frame Range / Clip Selection</h4>\n<p>Render only a portion of the motion.</p>\n<pre><code>visualize(\"motion.npy\", start_frame=50, end_frame=200)\n</code></pre>\n<h3>Why It Helps in Motion-S</h3>\n<ul>\n<li>Quickly check if your generated motions look <strong>physically plausible</strong> (no flying hands, reasonable speeds)</li>\n<li>Spot <strong>mode collapse</strong> or frozen joints early</li>\n<li>Compare your output vs. real KSL samples from train set</li>\n<li>Visualize <strong>fingertip trajectories</strong> — critical for sign language clarity</li>\n<li>Focus camera on <strong>hands / arms</strong> without heavy 3D rendering setup</li>\n</ul>",
      "rawMarkdown": "\nHello Motion-S participants! 👋\n\nAs many of you are starting to generate tokens → decode them with `rvq_vae_best.pth` → get `.npy` motion arrays, I wanted to share a small open-source helper tool we’ve just made public to make your life easier during development and debugging.\n\n### Kiseki (軌跡) – Trajectory Visualization\nRepo: https://github.com/signvrse/kiseki  \nLicense: MIT  \nDependencies: only **numpy** + **matplotlib** (no torch, no heavy motion libs)  \n\n### Quick Install (in your Kaggle notebook or local env)\n\n```bash\n!pip install git+https://github.com/signvrse/kiseki.git\n```\nKiseki takes your decoded `.npy` motion features (the same format you get from the VAE decoder: shape ≈ `[seq_len, num_joints*3]` or similar, root-relative or normalized) and turns them into:\n\n- Animated skeleton videos (.mp4)\n- Trajectory trails (see wrist / fingertip paths over time)\n- Hand-focused or upper-body zoomed views\n- Side-by-side or overlay comparison (great for generated vs. ground-truth)\n- Static key-frame grid PNGs\n- Frame-range clipping (debug only part of a long sequence)\n\nIt ships with a default BVH skeleton (`sample.bvh`) so you don’t need to provide one — but you can override it if your pipeline uses a different topology.\n\n### Usage \n\n#### To visualize a specific joint in a certain view\n```python\n# Focus on hands with front view\nvisualize(\"motion.npy\",\n          focus_joints='both_hands',\n          fixed_view='front',\n          fps=60)\n```\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F11979020%2F7ed71d22cdfc55626050dfc96fc6a8c3%2Fdownload%20(6).gif?generation=1770884908488554&alt=media)\n\n#### Compare two motion sequences (e.g. generated vs ground truth).\n\n```python\nfrom kiseki import compare\n\n# Overlay -- both skeletons on the same axes\ncompare(\"generated.npy\", \"ground_truth.npy\", mode=\"overlay\")\n\n# Side-by-side -- two panels\ncompare(\"generated.npy\", \"ground_truth.npy\", mode=\"side_by_side\")\n\n# With labels and fixed view\ncompare(\"a.npy\", \"b.npy\",\n        mode=\"side_by_side\",\n        fixed_view='front',\n        label_a=\"Generated\",\n        label_b=\"Ground Truth\")\n```\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F11979020%2F93774fd1cb3ae6851caa085aad300cd4%2Fdownload%20(7).gif?generation=1770885205056393&alt=media)\n\n#### Frame Range / Clip Selection\n\nRender only a portion of the motion.\n\n\n```python\nvisualize(\"motion.npy\", start_frame=50, end_frame=200)\n```\n### Why It Helps in Motion-S\n- Quickly check if your generated motions look **physically plausible** (no flying hands, reasonable speeds)\n- Spot **mode collapse** or frozen joints early\n- Compare your output vs. real KSL samples from train set\n- Visualize **fingertip trajectories** — critical for sign language clarity\n- Focus camera on **hands / arms** without heavy 3D rendering setup\n\n",
      "votes": 3
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "3405153": "\nHello Motion-S participants! 👋\n\nAs many of you are starting to generate tokens → decode them with `rvq_vae_best.pth` → get `.npy` motion arrays, I wanted to share a small open-source helper tool we’ve just made public to make your life easier during development and debugging.\n\n### Kiseki (軌跡) – Trajectory Visualization\nRepo: https://github.com/signvrse/kiseki  \nLicense: MIT  \nDependencies: only **numpy** + **matplotlib** (no torch, no heavy motion libs)  \n\n### Quick Install (in your Kaggle notebook or local env)\n\n```bash\n!pip install git+https://github.com/signvrse/kiseki.git\n```\nKiseki takes your decoded `.npy` motion features (the same format you get from the VAE decoder: shape ≈ `[seq_len, num_joints*3]` or similar, root-relative or normalized) and turns them into:\n\n- Animated skeleton videos (.mp4)\n- Trajectory trails (see wrist / fingertip paths over time)\n- Hand-focused or upper-body zoomed views\n- Side-by-side or overlay comparison (great for generated vs. ground-truth)\n- Static key-frame grid PNGs\n- Frame-range clipping (debug only part of a long sequence)\n\nIt ships with a default BVH skeleton (`sample.bvh`) so you don’t need to provide one — but you can override it if your pipeline uses a different topology.\n\n### Usage \n\n#### To visualize a specific joint in a certain view\n```python\n# Focus on hands with front view\nvisualize(\"motion.npy\",\n          focus_joints='both_hands',\n          fixed_view='front',\n          fps=60)\n```\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F11979020%2F7ed71d22cdfc55626050dfc96fc6a8c3%2Fdownload%20(6).gif?generation=1770884908488554&alt=media)\n\n#### Compare two motion sequences (e.g. generated vs ground truth).\n\n```python\nfrom kiseki import compare\n\n# Overlay -- both skeletons on the same axes\ncompare(\"generated.npy\", \"ground_truth.npy\", mode=\"overlay\")\n\n# Side-by-side -- two panels\ncompare(\"generated.npy\", \"ground_truth.npy\", mode=\"side_by_side\")\n\n# With labels and fixed view\ncompare(\"a.npy\", \"b.npy\",\n        mode=\"side_by_side\",\n        fixed_view='front',\n        label_a=\"Generated\",\n        label_b=\"Ground Truth\")\n```\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F11979020%2F93774fd1cb3ae6851caa085aad300cd4%2Fdownload%20(7).gif?generation=1770885205056393&alt=media)\n\n#### Frame Range / Clip Selection\n\nRender only a portion of the motion.\n\n\n```python\nvisualize(\"motion.npy\", start_frame=50, end_frame=200)\n```\n### Why It Helps in Motion-S\n- Quickly check if your generated motions look **physically plausible** (no flying hands, reasonable speeds)\n- Spot **mode collapse** or frozen joints early\n- Compare your output vs. real KSL samples from train set\n- Visualize **fingertip trajectories** — critical for sign language clarity\n- Focus camera on **hands / arms** without heavy 3D rendering setup\n\n"
  }
}