Add DrawMotion ZeroGPU Gradio adapter

README.md

@@ -1,13 +1,21 @@
 ---
 title: DrawMotion ZeroGPU
-emoji: 🏆
+emoji: 🏃
 colorFrom: blue
-colorTo: pink
+colorTo: green
 sdk: gradio
-sdk_version: 6.15.0
-python_version: '3.13'
+sdk_version: 5.49.1
 app_file: app.py
 pinned: false
+license: mit
+short_description: Text and trajectory conditioned 3D human motion generation.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# DrawMotion ZeroGPU
+
+This Space is a Gradio/ZeroGPU adapter for DrawMotion. It loads the public code
+from `InvertedForest/DrawMotion` and the public model assets from
+`I0u0I/DrawMotion`.
+
+The first version exposes text plus trajectory presets/custom JSON and returns an
+MP4 preview plus the generated joint JSON.

app.py

@@ -0,0 +1,180 @@
+import json
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+import gradio as gr
+import numpy as np
+from huggingface_hub import snapshot_download
+
+import spaces
+
+
+ROOT = Path(__file__).resolve().parent
+CODE_DIR = ROOT / "DrawMotion"
+MODEL_REPO = "I0u0I/DrawMotion"
+GIT_REPO = "https://github.com/InvertedForest/DrawMotion.git"
+
+ASSET_PATTERNS = [
+    "logs/human_ml3d/last.ckpt",
+    "mid_feat/t2m/mid_feat.pt",
+    "stickman/weight/real_init/t2m/stickman_encoder.ckpt",
+]
+
+EXAMPLES = {
+    "forward line": [[0, 0], [40, 0], [90, 0], [150, 0], [220, 0]],
+    "left arc": [[0, 0], [35, -20], [75, -55], [120, -90], [180, -115], [240, -120]],
+    "right arc": [[0, 0], [35, 20], [75, 55], [120, 90], [180, 115], [240, 120]],
+    "zigzag": [[0, 0], [45, -45], [90, 35], [135, -35], [180, 45], [230, 0]],
+    "circle": [[0, 0], [35, -55], [95, -75], [155, -45], [165, 20], [110, 55], [45, 45], [0, 0]],
+}
+
+runner = None
+
+
+def ensure_drawmotion_code():
+    if not CODE_DIR.exists():
+        subprocess.run(["git", "clone", "--depth", "1", GIT_REPO, str(CODE_DIR)], check=True)
+    snapshot_download(
+        repo_id=MODEL_REPO,
+        repo_type="model",
+        allow_patterns=ASSET_PATTERNS,
+        local_dir=CODE_DIR,
+    )
+    if str(CODE_DIR) not in sys.path:
+        sys.path.insert(0, str(CODE_DIR))
+    os.chdir(CODE_DIR)
+
+
+ensure_drawmotion_code()
+
+from demo.drawmotion_studio.app import validate_generate_payload
+from demo.drawmotion_studio.runner import DrawMotionRunner
+from mogen.utils.plot_utils import plot_3d_motion, t2m_kinematic_chain
+
+
+def get_runner():
+    global runner
+    if runner is None:
+        runner = DrawMotionRunner(
+            ckpt_path="logs/human_ml3d/last.ckpt",
+            gpu="0",
+            sample_index=0,
+            output_dir=str(ROOT / "runs"),
+        )
+    return runner
+
+
+def normalize_custom_points(custom_points):
+    points = json.loads(custom_points)
+    normalized = []
+    for point in points:
+        if isinstance(point, dict):
+            normalized.append({"x": float(point["x"]), "y": float(point["y"])})
+        else:
+            normalized.append({"x": float(point[0]), "y": float(point[1])})
+    return normalized
+
+
+def preset_points(name):
+    return [{"x": float(x), "y": float(y)} for x, y in EXAMPLES[name]]
+
+
+def format_result_json(result):
+    slim = dict(result)
+    slim["pred_joint"] = np.asarray(slim["pred_joint"]).round(5).tolist()
+    slim["input_trajectory"] = np.asarray(slim["input_trajectory"]).round(5).tolist()
+    slim["pred_trajectory"] = np.asarray(slim["pred_trajectory"]).round(5).tolist()
+    return json.dumps(slim, indent=2)
+
+
+@spaces.GPU(duration=300)
+def generate(text, trajectory_mode, custom_trajectory, frames, alpha, trajectory_scale, ifg_repeat, ifg_scale):
+    if trajectory_mode == "custom JSON":
+        trajectory = normalize_custom_points(custom_trajectory)
+    else:
+        trajectory = preset_points(trajectory_mode)
+
+    payload = {
+        "text": text,
+        "trajectory": trajectory,
+        "length": int(frames),
+        "density": float(alpha),
+        "trajectory_scale": float(trajectory_scale),
+        "ifg_repeat": int(ifg_repeat),
+        "ifg_scale": float(ifg_scale),
+        "stickmen": [],
+    }
+    payload = validate_generate_payload(payload)
+    result = get_runner().generate(payload)
+
+    run_dir = sorted((ROOT / "runs").iterdir())[-1]
+    video_path = run_dir / "motion.mp4"
+    plot_3d_motion(
+        str(video_path),
+        t2m_kinematic_chain,
+        np.asarray(result["pred_joint"], dtype=np.float32),
+        title=result["text"],
+        fps=20,
+    )
+
+    result_json = format_result_json(result)
+    result_path = run_dir / "result_for_download.json"
+    result_path.write_text(result_json, encoding="utf-8")
+    return str(video_path), result_json, str(result_path)
+
+
+def fill_custom_example(name):
+    if name == "custom JSON":
+        name = "left arc"
+    return json.dumps(EXAMPLES[name], indent=2)
+
+
+with gr.Blocks(title="DrawMotion") as demo:
+    gr.Markdown("# DrawMotion")
+    gr.Markdown("Text and trajectory conditioned 3D human motion generation.")
+    with gr.Row():
+        with gr.Column(scale=1):
+            text = gr.Textbox(
+                label="Text",
+                value="A person walks forward and turns left.",
+                lines=2,
+            )
+            trajectory_mode = gr.Dropdown(
+                choices=list(EXAMPLES.keys()) + ["custom JSON"],
+                value="left arc",
+                label="Trajectory",
+            )
+            custom_trajectory = gr.Textbox(
+                label="Custom trajectory JSON",
+                value=fill_custom_example("left arc"),
+                lines=8,
+            )
+            with gr.Row():
+                frames = gr.Slider(32, 196, value=120, step=1, label="Frames")
+                alpha = gr.Slider(0, 1, value=0.2, step=0.05, label="Alpha")
+            with gr.Row():
+                trajectory_scale = gr.Slider(20, 200, value=50, step=1, label="Trajectory scale")
+                ifg_repeat = gr.Slider(0, 100, value=50, step=1, label="IFG repeat")
+            ifg_scale = gr.Slider(0, 200, value=50, step=1, label="IFG scale")
+            run_button = gr.Button("Generate", variant="primary")
+        with gr.Column(scale=1):
+            video = gr.Video(label="Generated motion")
+            result_json = gr.Code(label="Result JSON", language="json", lines=18)
+            result_file = gr.File(label="Download result.json")
+
+    trajectory_mode.change(
+        fn=fill_custom_example,
+        inputs=trajectory_mode,
+        outputs=custom_trajectory,
+        show_progress="hidden",
+    )
+    run_button.click(
+        fn=generate,
+        inputs=[text, trajectory_mode, custom_trajectory, frames, alpha, trajectory_scale, ifg_repeat, ifg_scale],
+        outputs=[video, result_json, result_file],
+        concurrency_limit=1,
+    )
+
+demo.queue(max_size=8).launch()

packages.txt

@@ -0,0 +1,2 @@
+ffmpeg
+git

requirements.txt

@@ -0,0 +1,16 @@
+spaces
+gradio==5.49.1
+huggingface_hub
+git+https://github.com/openai/CLIP.git
+torch==2.8.0
+torchvision==0.23.0
+mmcv==1.7.2
+lightning==2.3.3
+einops==0.8.1
+matplotlib==3.10.3
+numpy==1.26.4
+opencv-python-headless==4.10.0.84
+packaging==25.0
+Pillow==11.2.1
+scipy==1.15.3
+tqdm==4.66.4