Initial MuscleMimic research space

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,17 @@
+# Python
+__pycache__/
+*.py[cod]
+*.egg-info/
+
+# Virtual environments
+.venv/
+venv/
+
+# OS / editor
+.DS_Store
+.idea/
+.vscode/
+
+# Gradio cache
+.gradio/
+flagged/

.python-version

@@ -0,0 +1 @@
+3.11

README.md

@@ -1,14 +1,24 @@
 ---
-title: Musclemimic Space
-emoji: 🔥
-colorFrom: pink
-colorTo: green
+title: MuscleMimic Research Space
+emoji: "\U0001F4C4"
+colorFrom: gray
+colorTo: blue
 sdk: gradio
-sdk_version: 6.13.0
+sdk_version: 5.35.0
 app_file: app.py
-pinned: false
+pinned: true
+models:
+  - amathislab/mm-fullbody-base
+  - amathislab/mm-bimanual-v0
+datasets:
+  - amathislab/musclemimic-retargeted
+  - amathislab/musclemimic-bimanual-retargeted
 license: apache-2.0
 short_description: MuscleMimic hugging face space
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# MuscleMimic Research Space
+
+A companion Space for [MuscleMimic](https://arxiv.org/abs/2603.25544) with rollout videos, checkpoint comparisons, and figures from the paper.
+
+Paper: https://huggingface.co/papers/2603.25544

app.py

@@ -0,0 +1,287 @@
+from html import escape
+from urllib.parse import quote
+
+import gradio as gr
+
+from config import (
+    BASE_DIR,
+    CATEGORIES,
+    CITATION_BIBTEX,
+    DEFAULT_MOTION,
+    HIGHLIGHT_VIDEOS,
+    MODELS,
+    MODEL_KEYS,
+    RESOURCE_LINKS,
+    get_motion_metadata,
+    get_motions_by_category,
+    get_video_path,
+    label_to_key,
+)
+
+gr.set_static_paths(paths=[str(BASE_DIR / "videos"), str(BASE_DIR / "assets")])
+FILE_ROUTE_PREFIX = "/gradio_api/file=" if int(gr.__version__.split(".", 1)[0]) >= 6 else "/file="
+
+with open(BASE_DIR / "style.css", encoding="utf-8") as css_file:
+    CUSTOM_CSS = css_file.read()
+
+
+def static_src(*relative_parts: str) -> str:
+    return file_src(BASE_DIR.joinpath(*relative_parts))
+
+
+def file_src(path) -> str:
+    return f"{FILE_ROUTE_PREFIX}{quote(str(path), safe='/')}"
+
+
+def render_resource_links() -> str:
+    cards = []
+    for item in RESOURCE_LINKS:
+        cards.append(
+            f"""
+            <a class="mm-link-card" href="{escape(item["url"])}" target="_blank" rel="noreferrer">
+                <span class="mm-link-title">{escape(item["label"])}</span>
+                <span class="mm-link-note">{escape(item["note"])}</span>
+            </a>
+            """
+        )
+    return "".join(cards)
+
+
+def build_hero_html() -> str:
+    return f"""
+    <section id="top" class="mm-hero">
+        <div class="mm-brand">
+            <img src="{static_src("assets", "logo.png")}" alt="MuscleMimic logo">
+            <span>MuscleMimic Research Space</span>
+        </div>
+        <h1>Checkpoint comparison and policy rollouts</h1>
+        <p class="mm-hero-lede">
+            MuscleMimic lets us train and evaluate many checkpoints and training recipes at scale.
+            Here we showcase a few interesting ones: side-by-side checkpoint comparisons and
+            policy rollouts across locomotion, manipulation, and dynamic motions.
+        </p>
+        <div class="mm-nav">
+            <a href="#explorer">Checkpoint comparison</a>
+            <a href="#highlights">Policy rollouts</a>
+            <a href="#resources">Paper and release links</a>
+            <a href="#citation">Citation</a>
+        </div>
+    </section>
+    """
+
+
+def build_explorer_intro_html() -> str:
+    return """
+    <section id="explorer" class="mm-section">
+        <div class="mm-section-head">
+            <p class="mm-overline">Checkpoint Comparison</p>
+            <h2>Checkpoint comparison on KIT locomotion motions</h2>
+            <p>
+                Compare MyoFullBody checkpoints on KIT locomotion motions.
+                The colored character is the reference motion and the other character is the rollout.
+            </p>
+        </div>
+    </section>
+    """
+
+
+def build_highlights_html() -> str:
+    cards = []
+    for item in HIGHLIGHT_VIDEOS:
+        cards.append(
+            f"""
+            <article class="mm-video-card">
+                <div class="mm-video-shell">
+                    <video autoplay loop muted playsinline preload="metadata">
+                        <source src="{static_src("videos", "blog", item["file"])}" type="video/mp4">
+                    </video>
+                </div>
+                <div class="mm-video-meta">
+                    <span class="mm-tag">{escape(item["tag"])}</span>
+                    <h3>{escape(item["label"])}</h3>
+                </div>
+            </article>
+            """
+        )
+
+    return f"""
+    <section id="highlights" class="mm-section">
+        <div class="mm-section-head">
+            <p class="mm-overline">Policy Rollouts</p>
+            <h2>Policy rollouts across locomotion, manipulation, and dynamic motions</h2>
+            <p>
+                Selected examples spanning locomotion, object interaction, and more dynamic motions.
+                In each video, the blue or purple character is the reference motion.
+            </p>
+        </div>
+        <div class="mm-video-grid">
+            {''.join(cards)}
+        </div>
+    </section>
+    """
+
+
+def build_resources_html() -> str:
+    return f"""
+    <section id="resources" class="mm-section">
+        <div class="mm-section-head">
+            <p class="mm-overline">Resources</p>
+            <h2>Paper and release links</h2>
+            <p>
+                Primary links for the paper, code, released checkpoint, and retargeted dataset.
+            </p>
+        </div>
+        <div class="mm-link-strip">
+            {render_resource_links()}
+        </div>
+    </section>
+    """
+
+
+def build_citation_html() -> str:
+    return f"""
+    <section id="citation" class="mm-section">
+        <div class="mm-section-head">
+            <p class="mm-overline">Citation</p>
+            <h2>Cite MuscleMimic</h2>
+            <p>
+                If you use MuscleMimic in your research, please cite the paper:
+            </p>
+        </div>
+        <pre class="mm-bibtex"><code>{escape(CITATION_BIBTEX)}</code></pre>
+    </section>
+    """
+
+
+def build_motion_summary_html(motion_label: str) -> str:
+    if not motion_label:
+        motion_label = DEFAULT_MOTION
+
+    motion_meta = get_motion_metadata(motion_label)
+
+    return f"""
+    <div class="mm-panel-head">
+        <div>
+            <p class="mm-overline">Selected Motion</p>
+            <h3>{escape(motion_label)}</h3>
+            <p>
+                Side-by-side comparison on a KIT locomotion trajectory from
+                <strong>{escape(motion_meta["category"])}</strong>.
+            </p>
+        </div>
+        <div class="mm-chip-row">
+            <span class="mm-chip">Dataset: {escape(motion_meta["dataset"])}</span>
+            <span class="mm-chip">Category: {escape(motion_meta["category"])}</span>
+            <span class="mm-chip">Reference: blue / purple actor</span>
+        </div>
+    </div>
+    """
+
+
+def get_motion_assets(motion_label: str):
+    if not motion_label:
+        motion_label = DEFAULT_MOTION
+
+    motion_key = label_to_key(motion_label)
+    return (
+        build_motion_summary_html(motion_label),
+        *[get_video_path(model_key, motion_key) for model_key in MODEL_KEYS],
+    )
+
+
+def update_explorer(category: str, current_motion: str):
+    choices = get_motions_by_category(category)
+    next_motion = current_motion if current_motion in choices else choices[0]
+    summary_html, *video_paths = get_motion_assets(next_motion)
+    return gr.Dropdown(choices=choices, value=next_motion), summary_html, *video_paths
+
+
+def update_motion_assets(motion_label: str):
+    return get_motion_assets(motion_label)
+
+
+def build_model_card_head(model_key: str) -> str:
+    model = MODELS[model_key]
+    return f"""
+    <div class="mm-compare-head">
+        <p class="mm-overline">Checkpoint</p>
+        <h3>{escape(model["label"])}</h3>
+        <p class="mm-compare-desc">{escape(model["description"])}</p>
+    </div>
+    """
+
+
+with gr.Blocks(title="MuscleMimic Research Space") as app:
+    gr.HTML(build_hero_html())
+    gr.HTML(build_explorer_intro_html())
+
+    with gr.Row(elem_classes="mm-controls-shell", equal_height=False):
+        category_dd = gr.Dropdown(
+            choices=CATEGORIES,
+            value="All",
+            label="Motion Family",
+            info="Filter the KIT / KINESIS set by locomotion subtype.",
+            elem_classes="mm-select",
+            scale=1,
+            min_width=280,
+        )
+        motion_dd = gr.Dropdown(
+            choices=get_motions_by_category("All"),
+            value=DEFAULT_MOTION,
+            label="Motion Clip",
+            info="Choose a KIT locomotion trajectory for checkpoint comparison.",
+            elem_classes="mm-select",
+            scale=1,
+            min_width=280,
+        )
+
+    _card_class_suffix = {
+        "mm-10m-1": "mm-compare-card-10m",
+        "mm-base-e": "mm-compare-card-base-e",
+        "mm-100M-s1": "mm-compare-card-100m",
+        "mm-fullbody-base": "mm-compare-card-fullbody-base",
+    }
+
+    initial = get_motion_assets(DEFAULT_MOTION)
+    initial_summary_html = initial[0]
+    initial_video_paths = list(initial[1:])
+
+    vids = []
+    with gr.Column(elem_classes="mm-compare-panel"):
+        selected_motion_html = gr.HTML(initial_summary_html)
+        with gr.Row(elem_classes="mm-compare-grid", equal_height=False):
+            for model_key, video_path in zip(MODEL_KEYS, initial_video_paths):
+                suffix = _card_class_suffix.get(model_key)
+                card_classes = ["mm-compare-card"]
+                if suffix:
+                    card_classes.append(suffix)
+                with gr.Column(elem_classes=card_classes):
+                    gr.HTML(build_model_card_head(model_key))
+                    vids.append(
+                        gr.Video(
+                            value=video_path,
+                            autoplay=True,
+                            loop=True,
+                            show_label=False,
+                            container=False,
+                            elem_classes="mm-compare-video",
+                        )
+                    )
+
+    gr.HTML(build_highlights_html())
+    gr.HTML(build_resources_html())
+    gr.HTML(build_citation_html())
+
+    category_dd.change(
+        fn=update_explorer,
+        inputs=[category_dd, motion_dd],
+        outputs=[motion_dd, selected_motion_html, *vids],
+    )
+    motion_dd.change(
+        fn=update_motion_assets,
+        inputs=[motion_dd],
+        outputs=[selected_motion_html, *vids],
+    )
+
+if __name__ == "__main__":
+    app.launch(css=CUSTOM_CSS)

config.py

@@ -0,0 +1,235 @@
+from pathlib import Path
+
+BASE_DIR = Path(__file__).parent
+
+MODELS = {
+    "mm-10m-1": {
+        "label": "MM-10M",
+        "accent": "#9a6237",
+        "description": "10M-parameter policy.",
+    },
+    "mm-base-e": {
+        "label": "MM-Base-E",
+        "accent": "#2f6a73",
+        "description": "Trained with a strong energy regularization.",
+    },
+    "mm-100M-s1": {
+        "label": "MM-100M",
+        "accent": "#7a4c8f",
+        "description": "100M-parameter policy.",
+    },
+    "mm-fullbody-base": {
+        "label": "MM-FullBody-Base",
+        "accent": "#c13a5b",
+        "description": "Released baseline checkpoint (50M parameters).",
+    },
+}
+
+MODEL_KEYS = list(MODELS.keys())
+
+MOTIONS = {
+    "KIT_314_walking_slow03_poses": {
+        "label": "Walking · Slow",
+        "category": "Walking",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_167_walking_medium03_poses": {
+        "label": "Walking · Medium",
+        "category": "Walking",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_424_walking_fast10_poses": {
+        "label": "Walking · Fast",
+        "category": "Walking",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_314_walking_run09_poses": {
+        "label": "Running",
+        "category": "Walking",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_11_WalkingStraightForwards05_poses": {
+        "label": "Walk Straight Forward",
+        "category": "Walking",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_4_WalkingStraightBackwards01_poses": {
+        "label": "Walk Backwards · Subject 4",
+        "category": "Backwards",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_6_WalkingStraightBackwards04_poses": {
+        "label": "Walk Backwards · Subject 6",
+        "category": "Backwards",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_7_WalkingStraightBackwards04_poses": {
+        "label": "Walk Backwards · Subject 7",
+        "category": "Backwards",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_10_WalkingStraightBackwards07_poses": {
+        "label": "Walk Backwards · Subject 10",
+        "category": "Backwards",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_11_WalkingStraightBackwards09_1_poses": {
+        "label": "Walk Backwards · Subject 11",
+        "category": "Backwards",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_12_WalkingStraightBackwards05_poses": {
+        "label": "Walk Backwards · Subject 12",
+        "category": "Backwards",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_3_turn_right04_poses": {
+        "label": "Turn Right · Subject 3",
+        "category": "Turning",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_8_RightTurn10_poses": {
+        "label": "Turn Right · Subject 8",
+        "category": "Turning",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_10_RightTurn02_poses": {
+        "label": "Turn Right · Subject 10",
+        "category": "Turning",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_348_turn_right06_poses": {
+        "label": "Turn Right · Subject 348",
+        "category": "Turning",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_424_turn_right02_poses": {
+        "label": "Turn Right · Subject 424",
+        "category": "Turning",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_7_LeftTurn06_poses": {
+        "label": "Turn Left · Subject 7",
+        "category": "Turning",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_11_LeftTurn05_poses": {
+        "label": "Turn Left · Subject 11",
+        "category": "Turning",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_12_LeftTurn03_poses": {
+        "label": "Turn Left · Subject 12",
+        "category": "Turning",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_359_turn_left05_poses": {
+        "label": "Turn Left · Subject 359",
+        "category": "Turning",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_4_WalkInClockwiseCircle04_poses": {
+        "label": "Clockwise Circle · Subject 4",
+        "category": "Circular",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_9_WalkInClockwiseCircle04_poses": {
+        "label": "Clockwise Circle · Subject 9a",
+        "category": "Circular",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_9_WalkInClockwiseCircle06_poses": {
+        "label": "Clockwise Circle · Subject 9b",
+        "category": "Circular",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_12_WalkInClockwiseCircle11_poses": {
+        "label": "Clockwise Circle · Subject 12",
+        "category": "Circular",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_6_WalkInCounterClockwiseCircle04_1_poses": {
+        "label": "Counter-Clockwise Circle · Subject 6a",
+        "category": "Circular",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_6_WalkInCounterClockwiseCircle06_1_poses": {
+        "label": "Counter-Clockwise Circle · Subject 6b",
+        "category": "Circular",
+        "dataset": "KIT / KINESIS",
+    },
+    "KIT_11_WalkInCounterClockwiseCircle02_poses": {
+        "label": "Counter-Clockwise Circle · Subject 11",
+        "category": "Circular",
+        "dataset": "KIT / KINESIS",
+    },
+}
+
+CATEGORIES = ["All", "Walking", "Backwards", "Turning", "Circular"]
+DEFAULT_MOTION = "Walking · Medium"
+
+_LABEL_TO_KEY = {metadata["label"]: motion_key for motion_key, metadata in MOTIONS.items()}
+
+
+def label_to_key(label: str) -> str:
+    return _LABEL_TO_KEY[label]
+
+
+def get_motion_metadata(label: str) -> dict[str, str]:
+    return MOTIONS[label_to_key(label)]
+
+
+def get_video_path(model_key: str, motion_key: str) -> str:
+    path = BASE_DIR / "videos" / "demo" / model_key / motion_key / "eval_MyoFullBody.mp4"
+    return str(path)
+
+
+def get_motions_by_category(category: str) -> list[str]:
+    if category == "All":
+        return [metadata["label"] for metadata in MOTIONS.values()]
+    return [
+        metadata["label"]
+        for metadata in MOTIONS.values()
+        if metadata["category"] == category
+    ]
+
+
+CITATION_BIBTEX = """@article{li2026towards,
+  title={Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale},
+  author={Li, Chengkun and Wang, Cheryl and Ziliotto, Bianca and Simos, Merkourios and Kovecses, Jozsef and Durandau, Guillaume and Mathis, Alexander},
+  journal={arXiv preprint arXiv:2603.25544},
+  year={2026}
+}"""
+
+
+RESOURCE_LINKS = [
+    {
+        "label": "Paper",
+        "url": "https://arxiv.org/abs/2603.25544",
+        "note": "Manuscript and supplementary figures",
+    },
+    {
+        "label": "Code",
+        "url": "https://github.com/amathislab/musclemimic",
+        "note": "Training and evaluation codebase",
+    },
+    {
+        "label": "Checkpoint",
+        "url": "https://huggingface.co/amathislab/mm-fullbody-base",
+        "note": "Released pretrained full-body model",
+    },
+    {
+        "label": "Dataset",
+        "url": "https://huggingface.co/datasets/amathislab/musclemimic-retargeted",
+        "note": "Retargeted motion dataset",
+    },
+]
+
+HIGHLIGHT_VIDEOS = [
+    {"file": "walking_running.mp4", "label": "Walking And Running", "tag": "Locomotion"},
+    {"file": "walking_turning.mp4", "label": "Walking And Turning", "tag": "Locomotion"},
+    {"file": "lifting_box.mp4", "label": "Lifting Box", "tag": "Manipulation"},
+    {"file": "drinking_water.mp4", "label": "Drinking Water", "tag": "Manipulation"},
+    {"file": "dancing.mp4", "label": "Dancing", "tag": "Dynamic Motion"},
+    {"file": "jumpingjack.mp4", "label": "Jumping Jack", "tag": "Dynamic Motion"},
+]

requirements.txt

@@ -0,0 +1 @@
+gradio>=5.0.0

style.css

@@ -0,0 +1,495 @@
+@import url("https://fonts.googleapis.com/css2?family=Manrope:wght@400;500;600;700;800&display=swap");
+
+:root {
+    --mm-bg: #f3f4f6;
+    --mm-surface: #ffffff;
+    --mm-surface-soft: #f8f9fb;
+    --mm-ink: #111827;
+    --mm-ink-soft: #4b5563;
+    --mm-muted: #6b7280;
+    --mm-line: #d9dee5;
+    --mm-accent: #244b57;
+    --mm-accent-soft: #eef4f6;
+    --mm-shadow: 0 10px 30px rgba(15, 23, 42, 0.06);
+    color-scheme: light;
+}
+
+html.dark,
+body.dark,
+:root[data-theme="dark"],
+html[data-theme="dark"],
+body[data-theme="dark"],
+.dark,
+.gradio-container.dark {
+    --mm-bg: #0b0f14;
+    --mm-surface: #121821;
+    --mm-surface-soft: #18212c;
+    --mm-ink: #f3f6fb;
+    --mm-ink-soft: #c8d2de;
+    --mm-muted: #96a3b3;
+    --mm-line: rgba(175, 189, 204, 0.18);
+    --mm-accent: #9cc1cb;
+    --mm-accent-soft: rgba(156, 193, 203, 0.14);
+    --mm-shadow: 0 16px 40px rgba(0, 0, 0, 0.28);
+    color-scheme: dark;
+}
+
+html,
+body {
+    margin: 0;
+    background: var(--mm-bg) !important;
+}
+
+body {
+    color: var(--mm-ink);
+    font-family: "Manrope", -apple-system, BlinkMacSystemFont, sans-serif;
+}
+
+.gradio-container {
+    max-width: 1180px !important;
+    margin: 0 auto !important;
+    padding: 24px 24px 56px !important;
+    background: transparent !important;
+    color: var(--mm-ink) !important;
+    font-family: "Manrope", -apple-system, BlinkMacSystemFont, sans-serif !important;
+    --body-background-fill: var(--mm-bg);
+    --body-background-fill-dark: var(--mm-bg);
+    --background-fill-primary: var(--mm-surface);
+    --background-fill-secondary: var(--mm-surface-soft);
+    --block-background-fill: var(--mm-surface);
+    --block-border-color: var(--mm-line);
+    --block-label-text-color: var(--mm-ink);
+    --block-title-text-color: var(--mm-ink);
+    --body-text-color: var(--mm-ink);
+    --body-text-color-subdued: var(--mm-muted);
+    --input-background-fill: var(--mm-surface-soft);
+    --input-border-color: var(--mm-line);
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