Initial commit

.dockerignore

@@ -0,0 +1,8 @@
+.git/
+__pycache__/
+*.py[cod]
+.ipynb_checkpoints/
+.gradio/
+.vscode/
+gradio/output/
+gradio/.gradio_tmp/

.gitattributes

@@ -0,0 +1,12 @@
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,13 @@
+__pycache__/
+*.py[cod]
+*.so
+.ipynb_checkpoints/
+
+.gradio/
+gradio/output/
+gradio/.gradio_tmp/
+.vscode/
+.DS_Store
+*.egg-info/
+.gradio_tmp/
+gradio/.gradio/

Dockerfile

@@ -0,0 +1,40 @@
+FROM python:3.11-slim
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    PIP_NO_CACHE_DIR=1 \
+    PYTHONUNBUFFERED=1 \
+    PORT=7860 \
+    GRADIO_SERVER_NAME=0.0.0.0 \
+    SATA_DEVICE=cpu \
+    SATA_GEN_DEVICE=cpu
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    git \
+    curl \
+    ffmpeg \
+    libglib2.0-0 \
+    libgl1 \
+    libgomp1 \
+    libsm6 \
+    libx11-6 \
+    libxext6 \
+    libxrender1 \
+    libegl1 \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+COPY . /app
+
+RUN python -m pip install --upgrade pip setuptools wheel
+RUN python -m pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
+RUN python -m pip install torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.5.1+cu121.html
+RUN python -m pip install torch_geometric==2.7.0
+RUN python -m pip install -r requirements-hf.txt
+RUN python -m pip install https://github.com/openai/CLIP/archive/refs/heads/main.zip
+RUN python -m pip install --no-build-isolation chumpy==0.70 && python scripts/patch_chumpy.py
+RUN python -m pip install -e .
+RUN mkdir -p /app/gradio/output /app/gradio/.gradio_tmp
+
+EXPOSE 7860
+CMD ["python", "app.py"]

LICENSE

@@ -0,0 +1,187 @@
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NOTICE

@@ -0,0 +1,31 @@
+SATA: Semantic-Aware Motion Encoding for Topology-Agnostic Character Animation
+Copyright 2026 Zongye Zhang and contributors
+
+The original SATA code in this repository is licensed under the Apache License,
+Version 2.0. See the LICENSE file for the full license text.
+
+Third-party submodules
+----------------------
+
+This repository references the following external projects as Git submodules.
+They are not relicensed by the top-level Apache License. Their use,
+redistribution, and citation requirements are governed by the licenses and
+notices in the corresponding upstream repositories:
+
+- src/fairmotion: https://github.com/zzysteve/fairmotion.git
+- src/mdm: https://github.com/zzysteve/SATA-motion-diffusion-model.git
+- src/momask-preenc: https://github.com/zzysteve/SATA-momask-codes.git
+
+Please review the license files in each submodule or upstream repository before
+using or redistributing those components.
+
+Files or assets outside the submodules that carry their own copyright or
+license notices remain governed by those notices and are not relicensed by this
+file.
+
+External artifacts
+------------------
+
+Model checkpoints, datasets, demo assets, body models, and other downloaded
+artifacts may have separate license terms. They are not covered by the top-level
+Apache License unless explicitly stated by their respective providers.

README.md

@@ -0,0 +1,76 @@
+---
+title: SATA Demo
+sdk: docker
+app_port: 7860
+suggested_hardware: cpu-basic
+pinned: false
+license: apache-2.0
+---
+
+# SATA: Semantic-Aware Motion Encoding for Topology-Agnostic Character Animation
+
+[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://zzysteve.github.io/project_pages/sata.html)
+[![arXiv](https://img.shields.io/badge/arXiv-b31b1b)](https://arxiv.org/abs/2605.27055)
+[![Code](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/zzysteve/SATA)
+
+Official Hugging Face Space demo for **Semantic-Aware Motion Encoding for Topology-Agnostic Character Animation**.
+
+SATA is a semantic-aware, topology-agnostic motion representation framework for heterogeneous character animation. It learns a unified latent motion manifold across diverse skeletal topologies and supports motion reconstruction, text-to-motion generation, and zero-shot cross-species retargeting.
+
+## Hugging Face Space
+
+This repository packages the Gradio demo for Hugging Face Spaces using the Docker SDK. The hosted Space is configured to run on CPU by default.
+
+This Space copy keeps deployment-specific runtime adjustments:
+
+- the app listens on the Hugging Face `PORT` environment variable, defaulting to `7860`;
+- CPU is used by default through `SATA_DEVICE=cpu` and `SATA_GEN_DEVICE=cpu`;
+- demo assets and checkpoints are included in the Space repository so the container can start without a separate artifact download step;
+- MDM text-to-latent generation avoids SMPL-backed xyz conversion because the demo only saves latent `z` outputs.
+
+If the Space is moved to GPU hardware later, set `SATA_DEVICE=cuda:0` and `SATA_GEN_DEVICE=cuda:0` in the Space environment.
+
+Run command inside the container:
+
+```shell
+python app.py
+```
+
+## Main Repository
+
+The source release is maintained at:
+
+```text
+https://github.com/zzysteve/SATA
+```
+
+## Citation
+
+```bibtex
+@inproceedings{zhang2026sata,
+  title     = {Semantic-Aware Motion Encoding for Topology-Agnostic Character Animation},
+  author    = {Zongye Zhang and Yuzhuo Cui and Qingjie Liu and Yunhong Wang},
+  booktitle = {Proceedings of the International Conference on Machine Learning},
+  year      = {2026},
+  note      = {To appear. arXiv:2605.27055},
+  url       = {https://arxiv.org/abs/2605.27055},
+}
+```
+
+## Contact
+
+For questions about the project, please contact the authors or open an issue after the initial code release.
+
+## License
+
+The original SATA code in this repository is released under the Apache License 2.0. See [LICENSE](LICENSE) and [NOTICE](NOTICE).
+
+The following bundled external components remain governed by their respective upstream licenses:
+
+```text
+src/fairmotion       https://github.com/zzysteve/fairmotion.git
+src/mdm              https://github.com/zzysteve/SATA-motion-diffusion-model.git
+src/momask-preenc    https://github.com/zzysteve/SATA-momask-codes.git
+```
+
+Please refer to the license files in each component or upstream repository before using or redistributing those components. Files or assets that carry their own copyright or license notices remain governed by those notices. Model checkpoints, datasets, demo assets, body models, and other downloaded artifacts may have separate terms and are not covered by the top-level Apache License unless explicitly stated by their providers.

app.py

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+"""
+SATA Demo
+
+Retarget:
+  Task 1: Human -> Human
+  Task 2: Animo -> Animo
+  Task 3: Human -> Animo
+  Task 4: Animo -> Human
+
+Generation:
+  Task 5: Text -> Motion
+"""
+import os
+import sys
+import threading
+import time
+import uuid
+
+# Path setup
+RELEASE_DIR = os.path.dirname(os.path.abspath(__file__))
+GRADIO_DIR  = os.path.join(RELEASE_DIR, 'gradio')
+sys.path.insert(0, GRADIO_DIR)
+sys.path.insert(0, os.path.join(RELEASE_DIR, 'src'))
+sys.path.insert(0, os.path.join(RELEASE_DIR, 'src', 'fairmotion'))
+sys.path.insert(0, os.path.join(RELEASE_DIR, 'src', 'Visualization'))
+
+# Gradio 6 reads DEFAULT_TEMP_DIR at import time, so set it first.
+_gradio_tmp = os.path.join(GRADIO_DIR, ".gradio_tmp")
+os.makedirs(_gradio_tmp, exist_ok=True)
+os.environ["GRADIO_TEMP_DIR"] = _gradio_tmp
+
+import gradio as gr
+
+from config import (
+    OUTPUT_DIR, MODEL_TYPE, MODEL_EPOCH, DEVICE,
+    TASK1_Z_OPTIONS, TASK1_SKEL_OPTIONS,
+    TASK2_Z_OPTIONS, TASK2_SKEL_OPTIONS,
+    TASK3_PAIRS, TASK4_PAIRS,
+    GEN_DEVICE, GEN_DEFAULT_MOTION_FRAMES,
+    GEN_MIN_MOTION_FRAMES, GEN_MAX_MOTION_FRAMES,
+    GEN_MODEL_OPTIONS, HUMAN_GEN_SKEL_OPTIONS,
+)
+from bvh_stick_viewer import get_bvh_viewer_html, iframe_html_for_motion
+
+
+# ════════════════════════════════════════════════════════════
+# Model loading (lazy and thread-safe)
+# ════════════════════════════════════════════════════════════
+_model_state = None
+_model_lock  = threading.Lock()
+
+def get_model_state():
+    global _model_state
+    if _model_state is None:
+        with _model_lock:
+            if _model_state is None:
+                print("[model] Loading model...")
+                from sata.utils.model_loading import load_model_by_type
+                model, cfg, ms_dict = load_model_by_type(MODEL_TYPE, MODEL_EPOCH, DEVICE)
+                model = model.to(DEVICE)
+                model.eval()
+                _model_state = (model, cfg, ms_dict)
+                print("[model] Model loaded.")
+    return _model_state
+
+
+# ════════════════════════════════════════════════════════════
+# Decode and render helpers
+# ════════════════════════════════════════════════════════════
+VIEWER_H = 360
+
+
+def empty_viewer(title):
+    return iframe_html_for_motion(None, title=title, height=VIEWER_H)
+
+
+def option_label(options, key):
+    return options[key].get('label', key)
+
+
+def dropdown_choices(options):
+    return [option_label(options, key) for key in options.keys()]
+
+
+def default_choice(options):
+    return option_label(options, list(options.keys())[0])
+
+
+def unique_output_name(base_name):
+    stamp = time.strftime('%y%m%d%H%M%S')
+    suffix = uuid.uuid4().hex[:8]
+    return f"{base_name}__{stamp}_{suffix}"
+
+
+def resolve_option_key(options, selected):
+    if selected in options:
+        return selected
+    for key in options.keys():
+        if option_label(options, key) == selected:
+            return key
+    raise KeyError(selected)
+
+
+def run_retarget(z_path, skel_path, output_name, task_name):
+    """
+    Decode to BVH and return the Three.js stick viewer.
+    """
+    from decode_z_sliding import decode_z_skel_to_bvh
+
+    task_out_dir = os.path.join(OUTPUT_DIR, task_name)
+    os.makedirs(task_out_dir, exist_ok=True)
+
+    try:
+        bvh_path = decode_z_skel_to_bvh(
+            z_path=z_path,
+            skel_path=skel_path,
+            model_epoch=MODEL_EPOCH,
+            output_dir=task_out_dir,
+            output_name=output_name,
+            device=DEVICE,
+            model_type=MODEL_TYPE,
+            model_state=get_model_state(),
+        )
+        return get_bvh_viewer_html(
+            bvh_path, title=output_name, height=VIEWER_H
+        )
+    except Exception as e:
+        print(f"[error] retarget failed: {e}")
+        import traceback; traceback.print_exc()
+        return empty_viewer("Retarget failed")
+
+
+# ════════════════════════════════════════════════════════════
+# Tab callbacks
+# ════════════════════════════════════════════════════════════
+
+# ── Task 1 ──────────────────────────────────────────────────
+def t1_src_change(z_key):
+    z_key = resolve_option_key(TASK1_Z_OPTIONS, z_key)
+    info = TASK1_Z_OPTIONS[z_key]
+    return get_bvh_viewer_html(
+        info['src_bvh'], title=f"Source: {option_label(TASK1_Z_OPTIONS, z_key)}", height=VIEWER_H,
+    )
+
+def t1_tgt_change(skel_key):
+    skel_key = resolve_option_key(TASK1_SKEL_OPTIONS, skel_key)
+    info = TASK1_SKEL_OPTIONS[skel_key]
+    return get_bvh_viewer_html(
+        info['tgt_bvh'], title=f"Target skeleton: {option_label(TASK1_SKEL_OPTIONS, skel_key)}",
+        height=VIEWER_H, max_frames=1,
+    )
+
+def t1_run(z_key, skel_key):
+    z_key = resolve_option_key(TASK1_Z_OPTIONS, z_key)
+    skel_key = resolve_option_key(TASK1_SKEL_OPTIONS, skel_key)
+    z    = TASK1_Z_OPTIONS[z_key]
+    skel = TASK1_SKEL_OPTIONS[skel_key]
+    name = unique_output_name(f"{z['name']}__{skel['name']}")
+    return run_retarget(z['z_path'], skel['skel_path'], name, 'task1')
+
+# ── Task 2 ──────────────────────────────────────────────────
+def t2_src_change(z_key):
+    z_key = resolve_option_key(TASK2_Z_OPTIONS, z_key)
+    info = TASK2_Z_OPTIONS[z_key]
+    return get_bvh_viewer_html(
+        info['src_bvh'], title=f"Source: {option_label(TASK2_Z_OPTIONS, z_key)}", height=VIEWER_H,
+    )
+
+def t2_tgt_change(skel_key):
+    skel_key = resolve_option_key(TASK2_SKEL_OPTIONS, skel_key)
+    info = TASK2_SKEL_OPTIONS[skel_key]
+    return get_bvh_viewer_html(
+        info['tgt_bvh'], title=f"Target skeleton: {option_label(TASK2_SKEL_OPTIONS, skel_key)}",
+        height=VIEWER_H, max_frames=1,
+    )
+
+def t2_run(z_key, skel_key):
+    z_key = resolve_option_key(TASK2_Z_OPTIONS, z_key)
+    skel_key = resolve_option_key(TASK2_SKEL_OPTIONS, skel_key)
+    z    = TASK2_Z_OPTIONS[z_key]
+    skel = TASK2_SKEL_OPTIONS[skel_key]
+    name = unique_output_name(f"{z['name']}__{skel['name']}")
+    return run_retarget(z['z_path'], skel['skel_path'], name, 'task2')
+
+# ── Task 3 ──────────────────────────────────────────────────
+def t3_pair_change(pair_key):
+    pair_key = resolve_option_key(TASK3_PAIRS, pair_key)
+    pair = TASK3_PAIRS[pair_key]
+    label = option_label(TASK3_PAIRS, pair_key)
+    src = get_bvh_viewer_html(
+        pair['src_bvh'], title=f"Source: {label}", height=VIEWER_H,
+    )
+    tgt = get_bvh_viewer_html(
+        pair['tgt_bvh'], title=f"Target skeleton: {label}",
+        height=VIEWER_H, max_frames=1,
+    )
+    return src, tgt
+
+def t3_run(pair_key):
+    pair_key = resolve_option_key(TASK3_PAIRS, pair_key)
+    pair = TASK3_PAIRS[pair_key]
+    name = unique_output_name(pair['output_name'])
+    return run_retarget(pair['z_path'], pair['skel_path'], name, 'task3')
+
+# ── Task 4 ──────────────────────────────────────────────────
+def t4_pair_change(pair_key):
+    pair_key = resolve_option_key(TASK4_PAIRS, pair_key)
+    pair = TASK4_PAIRS[pair_key]
+    label = option_label(TASK4_PAIRS, pair_key)
+    src = get_bvh_viewer_html(
+        pair['src_bvh'], title=f"Source: {label}", height=VIEWER_H,
+    )
+    tgt = get_bvh_viewer_html(
+        pair['tgt_bvh'], title=f"Target skeleton: {label}",
+        height=VIEWER_H, max_frames=1,
+    )
+    return src, tgt
+
+def t4_run(pair_key):
+    pair_key = resolve_option_key(TASK4_PAIRS, pair_key)
+    pair = TASK4_PAIRS[pair_key]
+    name = unique_output_name(pair['output_name'])
+    return run_retarget(pair['z_path'], pair['skel_path'], name, 'task4')
+
+
+# ════════════════════════════════════════════════════════════
+# Task 5: generation callbacks
+# ════════════════════════════════════════════════════════════
+def run_generation(text: str, skel_key: str, generation_label: str,
+                   motion_frames: int, skel_options: dict) -> str:
+    """
+    Generation pipeline: text -> latent/code -> BVH -> viewer.
+    """
+    from gen_runner import generate_motion_from_text
+
+    text = text.strip()
+    if not text:
+        return empty_viewer("Enter a text prompt")
+
+    motion_frames = int(round(float(motion_frames)))
+    motion_frames = min(max(motion_frames, GEN_MIN_MOTION_FRAMES), GEN_MAX_MOTION_FRAMES)
+
+    if not skel_key:
+        skel_key = list(skel_options.keys())[0]
+    else:
+        skel_key = resolve_option_key(skel_options, skel_key)
+
+    skel_info   = skel_options[skel_key]
+    skel_path   = skel_info['skel_path']
+    display_name = unique_output_name(skel_info['name'])
+    generation_type = GEN_MODEL_OPTIONS[generation_label]
+    gen_out_dir = os.path.join(OUTPUT_DIR, 'generation', generation_type)
+    os.makedirs(gen_out_dir, exist_ok=True)
+
+    try:
+        bvh_path = generate_motion_from_text(
+            text=text,
+            skel_npz_path=skel_path,
+            generation_type=generation_type,
+            output_dir=gen_out_dir,
+            device_str=GEN_DEVICE,
+            skel_name=skel_info['name'],
+            motion_frames=motion_frames,
+        )
+        if not bvh_path:
+            return empty_viewer("Generation failed")
+        return get_bvh_viewer_html(
+            bvh_path,
+            title=f"{display_name} - {generation_label} - {motion_frames} frames - {text}",
+            height=VIEWER_H,
+        )
+    except Exception as e:
+        print(f"[error] generation failed: {e}")
+        import traceback; traceback.print_exc()
+        return empty_viewer("Generation failed")
+
+
+def t5_run(text, skel_key, generation_label, motion_frames):
+    return run_generation(text, skel_key, generation_label, motion_frames, HUMAN_GEN_SKEL_OPTIONS)
+
+
+def t5_tgt_change(skel_key):
+    info = HUMAN_GEN_SKEL_OPTIONS[skel_key]
+    return get_bvh_viewer_html(
+        info['tgt_bvh'], title=f"Target skeleton: {skel_key}",
+        height=VIEWER_H, max_frames=1,
+    )
+
+
+# ════════════════════════════════════════════════════════════
+# Page-load previews
+# ════════════════════════════════════════════════════════════
+def on_load():
+    try:
+        return (
+            t1_src_change(list(TASK1_Z_OPTIONS.keys())[0]),
+            t1_tgt_change(list(TASK1_SKEL_OPTIONS.keys())[0]),
+            empty_viewer("Source motion"),
+            empty_viewer("Target skeleton"),
+            empty_viewer("Source human motion"),
+            empty_viewer("Target animal skeleton"),
+            empty_viewer("Source animal motion"),
+            empty_viewer("Target human skeleton"),
+            t5_tgt_change('mousey'),
+        )
+    except Exception as e:
+        print(f"[on_load] Preview preload failed, possibly because a BVH file is missing: {e}")
+        return (None,) * 9
+
+
+# ════════════════════════════════════════════════════════════
+# Gradio UI
+# ════════════════════════════════════════════════════════════
+DEFAULT_TEXT_PROMPT = "A person walks forward, bends down to pick something up off the ground."
+
+
+with gr.Blocks(title="SATA Demo") as app:
+
+    gr.Markdown(
+        """
+        # SATA: Semantic-Aware Motion Encoding for Topology-Agnostic Character Animation
+        Interactive visualization for motion retargeting and text-to-motion generation.
+        """
+    )
+
+    gr.Markdown("## Motion Retargeting")
+
+    with gr.Tabs() as retarget_tabs:
+
+        # ╔══════════════════════════════════════════════════╗
+        # ║  Task 1: Human -> Human                          ║
+        # ╚══════════════════════════════════════════════════╝
+        with gr.TabItem("Task 1 - Human -> Human") as t1_tab:
+            gr.Markdown("**Free selection**: choose any source motion and target skeleton.")
+            with gr.Row():
+                t1_z_dd   = gr.Dropdown(dropdown_choices(TASK1_Z_OPTIONS),
+                                        value=default_choice(TASK1_Z_OPTIONS),
+                                        label="Source motion", scale=2)
+                t1_sk_dd  = gr.Dropdown(dropdown_choices(TASK1_SKEL_OPTIONS),
+                                        value=default_choice(TASK1_SKEL_OPTIONS),
+                                        label="Target skeleton", scale=2)
+                t1_btn    = gr.Button("Run Model", variant="primary", scale=1)
+            with gr.Row():
+                t1_v_src  = gr.HTML(value=empty_viewer("Source motion"))
+                t1_v_tgt  = gr.HTML(value=empty_viewer("Target skeleton"))
+                t1_v_out  = gr.HTML(value=empty_viewer("Retarget result"))
+
+            t1_z_dd.change(t1_src_change,  t1_z_dd,           t1_v_src)
+            t1_sk_dd.change(t1_tgt_change, t1_sk_dd,          t1_v_tgt)
+            t1_btn.click(t1_run,           [t1_z_dd, t1_sk_dd], t1_v_out)
+
+        # ╔══════════════════════════════════════════════════╗
+        # ║  Task 2: Animo -> Animo                          ║
+        # ╚══════════════════════════════════════════════════╝
+        with gr.TabItem("Task 2 - Animo -> Animo") as t2_tab:
+            gr.Markdown("**Zero-shot retargeting**: choose any animal source motion and target skeleton.")
+            with gr.Row():
+                t2_z_dd   = gr.Dropdown(dropdown_choices(TASK2_Z_OPTIONS),
+                                        value=default_choice(TASK2_Z_OPTIONS),
+                                        label="Source motion", scale=2)
+                t2_sk_dd  = gr.Dropdown(dropdown_choices(TASK2_SKEL_OPTIONS),
+                                        value=default_choice(TASK2_SKEL_OPTIONS),
+                                        label="Target skeleton", scale=2)
+                t2_btn    = gr.Button("Run Model", variant="primary", scale=1)
+            with gr.Row():
+                t2_v_src  = gr.HTML(value=empty_viewer("Source motion"))
+                t2_v_tgt  = gr.HTML(value=empty_viewer("Target skeleton"))
+                t2_v_out  = gr.HTML(value=empty_viewer("Retarget result"))
+
+            t2_z_dd.change(t2_src_change,  t2_z_dd,           t2_v_src)
+            t2_sk_dd.change(t2_tgt_change, t2_sk_dd,          t2_v_tgt)
+            t2_btn.click(t2_run,           [t2_z_dd, t2_sk_dd], t2_v_out)
+
+        # ╔══════════════════════════════════════════════════╗
+        # ║  Task 3: Human -> Animo                          ║
+        # ╚══════════════════════════════════════════════════╝
+        with gr.TabItem("Task 3 - Human -> Animo") as t3_tab:
+            gr.Markdown("**Zero-shot retargeting**: selected human-to-animal samples.")
+            with gr.Row():
+                t3_dd     = gr.Dropdown(dropdown_choices(TASK3_PAIRS),
+                                        value=default_choice(TASK3_PAIRS),
+                                        label="Sample pair", scale=3)
+                t3_btn    = gr.Button("Run Model", variant="primary", scale=1)
+            with gr.Row():
+                t3_v_src  = gr.HTML(value=empty_viewer("Source human motion"))
+                t3_v_tgt  = gr.HTML(value=empty_viewer("Target animal skeleton"))
+                t3_v_out  = gr.HTML(value=empty_viewer("Retarget result"))
+
+            t3_dd.change(t3_pair_change, t3_dd, [t3_v_src, t3_v_tgt])
+            t3_btn.click(t3_run,         t3_dd,  t3_v_out)
+
+        # ╔══════════════════════════════════════════════════╗
+        # ║  Task 4: Animo -> Human                          ║
+        # ╚══════════════════════════════════════════════════╝
+        with gr.TabItem("Task 4 - Animo -> Human") as t4_tab:
+            gr.Markdown("**Zero-shot retargeting**: selected animal-to-human samples.")
+            with gr.Row():
+                t4_dd     = gr.Dropdown(dropdown_choices(TASK4_PAIRS),
+                                        value=default_choice(TASK4_PAIRS),
+                                        label="Sample pair", scale=3)
+                t4_btn    = gr.Button("Run Model", variant="primary", scale=1)
+            with gr.Row():
+                t4_v_src  = gr.HTML(value=empty_viewer("Source animal motion"))
+                t4_v_tgt  = gr.HTML(value=empty_viewer("Target human skeleton"))
+                t4_v_out  = gr.HTML(value=empty_viewer("Retarget result"))
+
+            t4_dd.change(t4_pair_change, t4_dd, [t4_v_src, t4_v_tgt])
+            t4_btn.click(t4_run,         t4_dd,  t4_v_out)
+
+        # Refresh source/target previews whenever a tab is selected.
+        t1_tab.select(
+            lambda z_key, skel_key: (t1_src_change(z_key), t1_tgt_change(skel_key)),
+            [t1_z_dd, t1_sk_dd],
+            [t1_v_src, t1_v_tgt],
+        )
+        t2_tab.select(
+            lambda z_key, skel_key: (t2_src_change(z_key), t2_tgt_change(skel_key)),
+            [t2_z_dd, t2_sk_dd],
+            [t2_v_src, t2_v_tgt],
+       )
+        t3_tab.select(t3_pair_change, t3_dd, [t3_v_src, t3_v_tgt])
+        t4_tab.select(t4_pair_change, t4_dd, [t4_v_src, t4_v_tgt])
+
+    # ════════════════════════════════════════════════════════
+    # Generation
+    # ════════════════════════════════════════════════════════
+    gr.Markdown("---")
+    gr.Markdown("## Text-to-motion Generation")
+    gr.Markdown(
+        "Enter a text prompt, choose a target skeleton and generation method, then click **Generate**."
+    )
+
+    with gr.Tabs():
+
+        # ╔══════════════════════════════════════════════════╗
+        # ║  Task 5: Text-to-motion generation               ║
+        # ╚══════════════════════════════════════════════════╝
+        with gr.TabItem("Task 5 - Text-to-motion Generation"):
+            gr.Markdown("Generate a motion from text and decode it onto the selected character skeleton.")
+            gr.Markdown("Generation models MDM and MoMask are large; each run can take about 2 minutes.")
+            with gr.Row():
+                t5_text = gr.Textbox(
+                    label="Text prompt (English)",
+                    value=DEFAULT_TEXT_PROMPT,
+                    lines=2,
+                )
+            with gr.Row():
+                t5_sk_dd = gr.Dropdown(
+                    dropdown_choices(HUMAN_GEN_SKEL_OPTIONS),
+                    value="mousey",
+                    label="Target skeleton",
+                    scale=2,
+                )
+                t5_model_dd = gr.Dropdown(
+                    list(GEN_MODEL_OPTIONS.keys()),
+                    value="RVQ",
+                    label="Method",
+                    scale=1,
+                )
+                t5_motion_frames = gr.Slider(
+                    minimum=GEN_MIN_MOTION_FRAMES,
+                    maximum=GEN_MAX_MOTION_FRAMES,
+                    value=GEN_DEFAULT_MOTION_FRAMES,
+                    step=1,
+                    label="Motion length",
+                    scale=2,
+                )
+                t5_btn = gr.Button("Generate", variant="primary", scale=1)
+            with gr.Row():
+                t5_v_tgt = gr.HTML(value=empty_viewer("Selected target skeleton"))
+                t5_v_out = gr.HTML(value=empty_viewer("Generation result"))
+
+            t5_sk_dd.change(t5_tgt_change, t5_sk_dd, t5_v_tgt)
+            t5_btn.click(t5_run, [t5_text, t5_sk_dd, t5_model_dd, t5_motion_frames], t5_v_out)
+
+    # Preload default interactive previews.
+    app.load(
+        fn=on_load,
+        outputs=[t1_v_src, t1_v_tgt,
+                 t2_v_src, t2_v_tgt,
+                 t3_v_src, t3_v_tgt,
+                 t4_v_src, t4_v_tgt,
+                 t5_v_tgt],
+    )
+
+
+def _env_flag(name: str, default: bool = False) -> bool:
+    value = os.environ.get(name)
+    if value is None:
+        return default
+    return value.strip().lower() in {"1", "true", "yes", "on"}
+
+
+if __name__ == "__main__":
+    app.launch(
+        server_name=os.environ.get("GRADIO_SERVER_NAME", "0.0.0.0"),
+        server_port=int(os.environ.get("PORT", "7860")),
+        share=_env_flag("GRADIO_SHARE", False),
+        debug=_env_flag("GRADIO_DEBUG", False),
+        show_error=True,
+        theme=gr.themes.Soft(primary_hue="blue"),
+    )

config/animo_train.yml

@@ -0,0 +1,94 @@
+train_data:
+  dir: zooV2_train_wtextV2/motion/processed
+  mask: []
+
+test_data:
+  dir: zooV2_test_wtextV2/motion/processed
+  sample_num: 50  # Use only the first 50 samples for quick tests
+
+representation:
+  skel: [lo, go, tf] # S
+  pose: [q, p, r, pv, qv, c] # D_t
+  out: [r, q, c] # \hat{D_t}
+
+model:
+  z_dim: 128
+  Encoder:
+      type: GPSEncTemporal
+      hidden_dim: 256     # Fixed hidden dimension for all GPS layers
+      num_layers: 3       # Number of GPS layers
+      heads: 4            # Number of attention heads (for spatial GPS)
+      attn_type: multihead
+      attn_kwargs:
+        dropout: 0.1
+      # Temporal Transformer parameters (NEW!)
+      temporal_type: transformer     # 'conv' or 'transformer'
+      temporal_num_heads: 4          # Number of attention heads for temporal
+      temporal_dropout: 0.1
+      temporal_ff_dim_multiplier: 2  # FFN hidden layer multiplier
+      use_temporal_pos_encoding: True  # Use positional encoding
+      pos_encoding_type: sinusoidal  # 'sinusoidal' (extrapolates) or 'learnable' (fixed length)
+      max_seq_len: 512               # Only needed for learnable encoding
+      sample_posterior: True
+  Decoder:
+      type: GPSDecTemporal
+      hidden_dim: 256     # Fixed hidden dimension for all GPS layers
+      num_layers: 3       # Number of GPS layers
+      heads: 4            # Number of attention heads (for spatial GPS)
+      attn_type: multihead
+      attn_kwargs:
+        dropout: 0.1
+      # Temporal Transformer parameters (NEW!)
+      temporal_type: transformer     # 'conv' or 'transformer'
+      temporal_num_heads: 4          # Number of attention heads for temporal
+      temporal_dropout: 0.1
+      temporal_ff_dim_multiplier: 2  # FFN hidden layer multiplier
+      use_temporal_pos_encoding: True  # Use positional encoding
+      pos_encoding_type: sinusoidal  # 'sinusoidal' (extrapolates) or 'learnable' (fixed length)
+      max_seq_len: 512               # Only needed for learnable encoding
+  # load: 
+  #   - dir: blahblah
+  #     epoch: 
+  #     prefix: encoder
+  #     freeze: True
+
+train:
+  consq_n: 64
+  min_motion_lens: 20
+  batch_size: 12 # 16 if GPU memory allows
+  grad_max_norm: 0.5
+  learning_rate: 0.0003
+  
+  lr_schedule: 
+    type: exponential
+    gamma: 0.99
+    min: 0.01
+
+  epoch_num: 400
+  save_per: 5
+
+  # load trainer to continue training
+  # load:
+  #   dir: prev_trainer
+  #   epoch: None
+
+  # misc
+  copy_orig_contact: False
+
+  loss:
+    q: 5
+    p: 0.01
+    r: 10
+    pv: 1
+    c: 1
+    cv: 6
+    pen: 0.1
+    slide: 6
+    jerk: 0.2
+
+  metric: [qR, ra_xz, pa, slide, jerk, pen] 
+
+  vae_kl:
+    kl_weight: 0.000001
+    nll_loss_type: None
+

config/human_train.yml

@@ -0,0 +1,96 @@
+train_data:
+  dir: train_faceZ_mirror_wtextV3/motion/processed
+  mask: []
+
+test_data:
+  dir: test_recon_faceZ_mirror_wtextV3/motion/processed
+  sample_num: 50  # Use only the first 50 samples for quick tests
+
+representation:
+  skel: [lo, go, tf] # S
+  pose: [q, p, r, pv, qv, c] # D_t
+  out: [r, q, c] # \hat{D_t}
+
+model:
+  z_dim: 128
+  Encoder:
+      type: GPSEncTemporal
+      hidden_dim: 256     # Fixed hidden dimension for all GPS layers
+      num_layers: 3       # Number of GPS layers
+      heads: 4            # Number of attention heads (for spatial GPS)
+      attn_type: multihead
+      attn_kwargs:
+        dropout: 0.1
+      # Temporal Transformer parameters (NEW!)
+      temporal_type: transformer     # 'conv' or 'transformer'
+      temporal_num_heads: 4          # Number of attention heads for temporal
+      temporal_dropout: 0.1
+      temporal_ff_dim_multiplier: 2  # FFN hidden layer multiplier
+      use_temporal_pos_encoding: True  # Use positional encoding
+      pos_encoding_type: sinusoidal  # 'sinusoidal' (extrapolates) or 'learnable' (fixed length)
+      max_seq_len: 512               # Only needed for learnable encoding
+      consq_n: 64                    # Number of consecutive frames (must match train.consq_n)
+      sample_posterior: True
+  Decoder:
+      type: GPSDecTemporal
+      hidden_dim: 256     # Fixed hidden dimension for all GPS layers
+      num_layers: 3       # Number of GPS layers
+      heads: 4            # Number of attention heads (for spatial GPS)
+      attn_type: multihead
+      attn_kwargs:
+        dropout: 0.1
+      # Temporal Transformer parameters (NEW!)
+      temporal_type: transformer     # 'conv' or 'transformer'
+      temporal_num_heads: 4          # Number of attention heads for temporal
+      temporal_dropout: 0.1
+      temporal_ff_dim_multiplier: 2  # FFN hidden layer multiplier
+      use_temporal_pos_encoding: True  # Use positional encoding
+      pos_encoding_type: sinusoidal  # 'sinusoidal' (extrapolates) or 'learnable' (fixed length)
+      max_seq_len: 512               # Only needed for learnable encoding
+      consq_n: 64                    # Number of consecutive frames (must match train.consq_n)
+  # load: 
+  #   - dir: blahblah
+  #     epoch: 
+  #     prefix: encoder
+  #     freeze: True
+
+train:
+  consq_n: 64
+  min_motion_lens: 20
+  batch_size: 16 # 16 if GPU memory allows
+  grad_max_norm: 0.5
+  learning_rate: 0.0003
+  
+  lr_schedule: 
+    type: exponential
+    gamma: 0.99
+    min: 0.01
+
+  epoch_num: 400
+  save_per: 5
+
+  # load trainer to continue training
+  # load:
+  #   dir: prev_trainer
+  #   epoch: None
+
+  # misc
+  copy_orig_contact: False
+
+  loss:
+    q: 5
+    p: 0.01
+    r: 10
+    pv: 1
+    c: 1
+    cv: 6
+    pen: 0.1
+    slide: 6
+    jerk: 0.2
+
+  metric: [qR, ra_xz, pa, slide, jerk, pen] 
+
+  vae_kl:
+    kl_weight: 0.000001
+    nll_loss_type: None
+

config/multi_dataset_train.yml

@@ -0,0 +1,96 @@
+train_data:
+  dir: merge_train_faceZ_mirror_wtextV3_zooV2_train_wtextV2/motion/processed
+  mask: []
+
+test_data:
+  dir: test_recon_faceZ_mirror_wtextV3/motion/processed
+  sample_num: 50  # Use only the first 50 samples for quick tests
+
+representation:
+  skel: [lo, go, tf] # S
+  pose: [q, p, r, pv, qv, c] # D_t
+  out: [r, q, c] # \hat{D_t}
+
+model:
+  z_dim: 128
+  Encoder:
+      type: GPSEncTemporal
+      hidden_dim: 256     # Fixed hidden dimension for all GPS layers
+      num_layers: 3       # Number of GPS layers
+      heads: 4            # Number of attention heads (for spatial GPS)
+      attn_type: multihead
+      attn_kwargs:
+        dropout: 0.1
+      # Temporal Transformer parameters (NEW!)
+      temporal_type: transformer     # 'conv' or 'transformer'
+      temporal_num_heads: 4          # Number of attention heads for temporal
+      temporal_dropout: 0.1
+      temporal_ff_dim_multiplier: 2  # FFN hidden layer multiplier
+      use_temporal_pos_encoding: True  # Use positional encoding
+      pos_encoding_type: sinusoidal  # 'sinusoidal' (extrapolates) or 'learnable' (fixed length)
+      max_seq_len: 512               # Only needed for learnable encoding
+      consq_n: 64                    # Number of consecutive frames (must match train.consq_n)
+      sample_posterior: True
+  Decoder:
+      type: GPSDecTemporal
+      hidden_dim: 256     # Fixed hidden dimension for all GPS layers
+      num_layers: 3       # Number of GPS layers
+      heads: 4            # Number of attention heads (for spatial GPS)
+      attn_type: multihead
+      attn_kwargs:
+        dropout: 0.1
+      # Temporal Transformer parameters (NEW!)
+      temporal_type: transformer     # 'conv' or 'transformer'
+      temporal_num_heads: 4          # Number of attention heads for temporal
+      temporal_dropout: 0.1
+      temporal_ff_dim_multiplier: 2  # FFN hidden layer multiplier
+      use_temporal_pos_encoding: True  # Use positional encoding
+      pos_encoding_type: sinusoidal  # 'sinusoidal' (extrapolates) or 'learnable' (fixed length)
+      max_seq_len: 512               # Only needed for learnable encoding
+      consq_n: 64                    # Number of consecutive frames (must match train.consq_n)
+  # load: 
+  #   - dir: blahblah
+  #     epoch: 
+  #     prefix: encoder
+  #     freeze: True
+
+train:
+  consq_n: 64
+  min_motion_lens: 20
+  batch_size: 12 # 16 if GPU memory allows
+  grad_max_norm: 0.5
+  learning_rate: 0.0003
+  
+  lr_schedule: 
+    type: exponential
+    gamma: 0.99
+    min: 0.01
+
+  epoch_num: 400
+  save_per: 5
+
+  # load trainer to continue training
+  # load:
+  #   dir: prev_trainer
+  #   epoch: None
+
+  # misc
+  copy_orig_contact: False
+
+  loss:
+    q: 5
+    p: 0.01
+    r: 10
+    pv: 1
+    c: 1
+    cv: 6
+    pen: 0.1
+    slide: 6
+    jerk: 0.2
+
+  metric: [qR, ra_xz, pa, slide, jerk, pen] 
+
+  vae_kl:
+    kl_weight: 0.000001
+    nll_loss_type: None
+

config/sample.yml

@@ -0,0 +1,62 @@
+train_data:
+  dir: sample/motion/processed
+  mask: [src, tgt]
+
+representation:
+  skel: [lo, go] # S
+  pose: [q, p, r, pv, qv, pprev, c] # D_t
+  out: [r, q, c] # \hat{D_t}
+
+model:
+  z_dim: 32
+  Encoder:
+      type: GATEnc
+      hid_lyrs: [16, 16, 16]
+      heads_num: 16
+  Decoder:
+      type: GATDec
+      hid_lyrs: [16, 16, 16]
+      heads_num: 16
+      tgt_all_lyr: True
+  # load: 
+  #   - dir: blahblah
+  #     epoch: 
+  #     prefix: encoder
+  #     freeze: True
+
+train:
+  consq_n: 8
+  batch_size: 256 # 512 if GPU memory allows
+  grad_max_norm: 0.5
+  learning_rate: 0.01
+  
+  lr_schedule: 
+    type: exponential
+    gamma: 0.99
+    min: 0.01
+
+  epoch_num: 300
+  save_per: 30
+
+  # load trainer to continue training
+  # load:
+  #   dir: prev_trainer
+  #   epoch: None
+
+  # misc
+  copy_orig_contact: False
+
+  loss:
+    q: 5
+    p: 0.01
+    r: 10
+    pv: 1
+    c: 1
+    cv: 6
+    z: 1
+    pen: 0.1
+    slide: 6
+    jerk: 0.2
+
+  metric: [qR, ra_xz, pa, slide, jerk, pen] 
+

data/gradio_animo/bvh/dromedary_camel_male_runtostandturnl.bvh

@@ -0,0 +1,295 @@
+HIERARCHY
+ROOT joint_3652173982
+{
+	OFFSET 0.000000 0.000000 0.000000
+	CHANNELS 6 Xposition Yposition Zposition Xrotation Yrotation Zrotation
+	JOINT joint_1065643261
+	{
+		OFFSET 0.000000 0.251600 -0.000000
+		CHANNELS 3 Xrotation Yrotation Zrotation
+		JOINT joint_3369486670
+		{
+			OFFSET 0.000000 -5.031500 0.000000
+			CHANNELS 3 Xrotation Yrotation Zrotation
+			JOINT joint_602128083
+			{
+				OFFSET 0.000000 -24.972099 -1.180000
+				CHANNELS 3 Xrotation Yrotation Zrotation
+				JOINT joint_1164186991
+				{
+					OFFSET 0.000000 -24.868000 -2.565500
+					CHANNELS 3 Xrotation Yrotation Zrotation
+					JOINT joint_1661228954
+					{
+						OFFSET 0.000000 -27.949499 -2.633300
+						CHANNELS 3 Xrotation Yrotation Zrotation
+						JOINT joint_1393197377
+						{
+							OFFSET 0.000000 -31.537001 -8.536100
+							CHANNELS 3 Xrotation Yrotation Zrotation
+							JOINT joint_283295117
+							{
+								OFFSET 0.000000 -24.062300 -4.144600
+								CHANNELS 3 Xrotation Yrotation Zrotation
+								JOINT joint_172969524
+								{
+									OFFSET 0.000000 -24.337700 1.956700
+									CHANNELS 3 Xrotation Yrotation Zrotation
+									JOINT joint_722122187
+									{
+										OFFSET 0.000000 -16.444000 18.478701
+										CHANNELS 3 Xrotation Yrotation Zrotation
+										JOINT joint_482564213
+										{
+											OFFSET 0.000000 -8.510100 27.868700
+											CHANNELS 3 Xrotation Yrotation Zrotation
+											JOINT joint_2290643481
+											{
+												OFFSET 0.000000 -2.095100 -3.463800
+												CHANNELS 3 Xrotation Yrotation Zrotation
+												End Site
+												{
+													OFFSET 0.000000 -35.555618 -5.086917
+												}
+											}
+											JOINT joint_1816776104
+											{
+												OFFSET 9.829900 -16.800600 4.601600
+												CHANNELS 3 Xrotation Yrotation Zrotation
+												End Site
+												{
+													OFFSET 1.083200 -0.549100 0.000000
+												}
+											}
+											JOINT joint_1637253561
+											{
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+												CHANNELS 3 Xrotation Yrotation Zrotation
+												End Site
+												{
+													OFFSET -1.083200 -0.549100 0.000000
+												}
+											}
+										}
+									}
+								}
+							}
+						}
+						JOINT joint_3717329289
+						{
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+							CHANNELS 3 Xrotation Yrotation Zrotation
+							JOINT joint_2924335438
+							{
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+								CHANNELS 3 Xrotation Yrotation Zrotation
+								JOINT joint_1256690376
+								{
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+									CHANNELS 3 Xrotation Yrotation Zrotation
+									JOINT joint_452321840
+									{
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+										CHANNELS 3 Xrotation Yrotation Zrotation
+										JOINT joint_3029316820
+										{
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+											CHANNELS 3 Xrotation Yrotation Zrotation
+											End Site
+											{
+												OFFSET 2.136950 -7.658850 -9.307650
+											}
+										}
+									}
+									JOINT joint_1830591162
+									{
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+										CHANNELS 3 Xrotation Yrotation Zrotation
+										End Site
+										{
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+										}
+									}
+								}
+							}
+						}
+						JOINT joint_1864294951
+						{
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+							CHANNELS 3 Xrotation Yrotation Zrotation
+							JOINT joint_2523110081
+							{
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+								CHANNELS 3 Xrotation Yrotation Zrotation
+								JOINT joint_111892690
+								{
+									OFFSET 1.608900 22.300100 -28.437000
+									CHANNELS 3 Xrotation Yrotation Zrotation
+									JOINT joint_2213059258
+									{
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+										CHANNELS 3 Xrotation Yrotation Zrotation
+										JOINT joint_875639747
+										{
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+											CHANNELS 3 Xrotation Yrotation Zrotation
+											End Site
+											{
+												OFFSET -2.136950 -7.658800 -9.307700
+											}
+										}
+									}
+									JOINT joint_2864695845
+									{
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+										CHANNELS 3 Xrotation Yrotation Zrotation
+										End Site
+										{
+											OFFSET 0.102000 -1.328100 -15.132400
+										}
+									}
+								}
+							}
+						}
+					}
+				}
+			}
+		}
+		JOINT joint_3654464912
+		{
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+			CHANNELS 3 Xrotation Yrotation Zrotation
+			JOINT joint_4041021681
+			{
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+				CHANNELS 3 Xrotation Yrotation Zrotation
+				End Site
+				{
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+				}
+			}
+		}
+		JOINT joint_3736563935
+		{
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+			CHANNELS 3 Xrotation Yrotation Zrotation
+			JOINT joint_765483633
+			{
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+				CHANNELS 3 Xrotation Yrotation Zrotation
+				JOINT joint_887334106
+				{
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+					CHANNELS 3 Xrotation Yrotation Zrotation
+					JOINT joint_2694044589
+					{
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+						CHANNELS 3 Xrotation Yrotation Zrotation
+						End Site
+						{
+							OFFSET 2.670650 -9.349200 -13.122650
+						}
+					}
+				}
+				JOINT joint_131332004
+				{
+					OFFSET -3.107900 10.936700 -20.709600
+					CHANNELS 3 Xrotation Yrotation Zrotation
+					End Site
+					{
+						OFFSET -1.864800 6.562000 -12.425800
+					}
+				}
+			}
+			JOINT joint_3630937574
+			{
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+				CHANNELS 3 Xrotation Yrotation Zrotation
+				End Site
+				{
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+				}
+			}
+		}
+		JOINT joint_1060371951
+		{
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+			CHANNELS 3 Xrotation Yrotation Zrotation
+			JOINT joint_2091398058
+			{
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+				CHANNELS 3 Xrotation Yrotation Zrotation
+				JOINT joint_3955709968
+				{
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+					CHANNELS 3 Xrotation Yrotation Zrotation
+					JOINT joint_3894818105
+					{
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+						CHANNELS 3 Xrotation Yrotation Zrotation
+						End Site
+						{
+							OFFSET -2.670650 -9.349200 -13.122600
+						}
+					}
+				}
+				JOINT joint_105297424
+				{
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+					CHANNELS 3 Xrotation Yrotation Zrotation
+					End Site
+					{
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+					}
+				}
+			}
+			JOINT joint_4010156290
+			{
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+				CHANNELS 3 Xrotation Yrotation Zrotation
+				End Site
+				{
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+				}
+			}
+		}
+		JOINT joint_4044042034
+		{
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+			CHANNELS 3 Xrotation Yrotation Zrotation
+			End Site
+			{
+				OFFSET 0.000000 5.394500 -1.864600
+			}
+		}
+	}
+}
+MOTION
+Frames: 26
+Frame Time: 0.050000
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data/gradio_target_bvh/animo/fennec_fox_male_drinktostand.bvh

@@ -0,0 +1,308 @@
+HIERARCHY
+ROOT def_c_root_joint
+{
+	OFFSET 0.000000 0.000000 0.000000
+	CHANNELS 6 Xposition Yposition Zposition Xrotation Yrotation Zrotation
+	JOINT def_c_hips_joint
+	{
+		OFFSET 0.000000 0.240000 0.000000
+		CHANNELS 3 Xrotation Yrotation Zrotation
+		JOINT def_c_spine1_joint
+		{
+			OFFSET 0.000000 -0.240000 0.000000
+			CHANNELS 3 Xrotation Yrotation Zrotation
+			JOINT def_c_spine2_joint
+			{
+				OFFSET 0.000000 -4.208500 -0.528300
+				CHANNELS 3 Xrotation Yrotation Zrotation
+				JOINT def_c_spine3_joint
+				{
+					OFFSET 0.000000 -3.974000 -0.455200
+					CHANNELS 3 Xrotation Yrotation Zrotation
+					JOINT def_c_chest_joint
+					{
+						OFFSET 0.000000 -3.996600 -0.165500
+						CHANNELS 3 Xrotation Yrotation Zrotation
+						JOINT def_c_neck1_joint
+						{
+							OFFSET 0.000000 -4.152900 0.213600
+							CHANNELS 3 Xrotation Yrotation Zrotation
+							JOINT def_c_neck2_joint
+							{
+								OFFSET 0.000000 -3.335800 1.261100
+								CHANNELS 3 Xrotation Yrotation Zrotation
+								JOINT def_c_head_joint
+								{
+									OFFSET 0.000000 -3.075700 1.840500
+									CHANNELS 3 Xrotation Yrotation Zrotation
+									JOINT def_c_jaw_joint
+									{
+										OFFSET 0.000000 -0.227500 -0.877900
+										CHANNELS 3 Xrotation Yrotation Zrotation
+										End Site
+										{
+											OFFSET 0.000000 -4.642250 -3.061500
+										}
+									}
+									JOINT def_eye_joint.L
+									{
+										OFFSET 1.776500 -4.377100 -0.441200
+										CHANNELS 3 Xrotation Yrotation Zrotation
+										End Site
+										{
+											OFFSET 0.042500 -1.954800 -0.046400
+										}
+									}
+									JOINT def_eye_joint.R
+									{
+										OFFSET -1.776500 -4.377100 -0.441200
+										CHANNELS 3 Xrotation Yrotation Zrotation
+										End Site
+										{
+											OFFSET -0.042500 -1.954800 -0.046400
+										}
+									}
+								}
+							}
+						}
+						JOINT def_clavicle_joint.L
+						{
+							OFFSET 1.928800 -2.305700 3.049400
+							CHANNELS 3 Xrotation Yrotation Zrotation
+							JOINT def_frontLegUpr_joint.L
+							{
+								OFFSET 1.115100 -1.646200 -5.514800
+								CHANNELS 3 Xrotation Yrotation Zrotation
+								JOINT def_frontLegLwr_joint.L
+								{
+									OFFSET -0.174700 2.191500 -5.152200
+									CHANNELS 3 Xrotation Yrotation Zrotation
+									JOINT def_frontHorselink_joint.L
+									{
+										OFFSET -0.356300 0.440600 -8.066700
+										CHANNELS 3 Xrotation Yrotation Zrotation
+										JOINT def_frontFoot_joint.L
+										{
+											OFFSET 0.133600 -0.741500 -1.963400
+											CHANNELS 3 Xrotation Yrotation Zrotation
+											End Site
+											{
+												OFFSET 0.020200 -0.635000 -0.429540
+											}
+										}
+									}
+									JOINT def_frontLegLwrAllTwist_joint.L
+									{
+										OFFSET -0.178100 0.220300 -4.033400
+										CHANNELS 3 Xrotation Yrotation Zrotation
+										End Site
+										{
+											OFFSET -0.106900 0.132200 -2.420000
+										}
+									}
+								}
+							}
+						}
+						JOINT def_clavicle_joint.R
+						{
+							OFFSET -1.928800 -2.305800 3.049400
+							CHANNELS 3 Xrotation Yrotation Zrotation
+							JOINT def_frontLegUpr_joint.R
+							{
+								OFFSET -1.115100 -1.646200 -5.514700
+								CHANNELS 3 Xrotation Yrotation Zrotation
+								JOINT def_frontLegLwr_joint.R
+								{
+									OFFSET 0.174700 2.191500 -5.152200
+									CHANNELS 3 Xrotation Yrotation Zrotation
+									JOINT def_frontHorselink_joint.R
+									{
+										OFFSET 0.356300 0.440600 -8.066700
+										CHANNELS 3 Xrotation Yrotation Zrotation
+										JOINT def_frontFoot_joint.R
+										{
+											OFFSET -0.133600 -0.741600 -1.963400
+											CHANNELS 3 Xrotation Yrotation Zrotation
+											End Site
+											{
+												OFFSET -0.020200 -0.634940 -0.429540
+											}
+										}
+									}
+									JOINT def_frontLegLwrAllTwist_joint.R
+									{
+										OFFSET 0.178100 0.220300 -4.033400
+										CHANNELS 3 Xrotation Yrotation Zrotation
+										End Site
+										{
+											OFFSET 0.106900 0.132200 -2.420000
+										}
+									}
+								}
+							}
+						}
+					}
+				}
+			}
+		}
+		JOINT def_c_tail1_joint
+		{
+			OFFSET -0.000000 7.392700 -0.015700
+			CHANNELS 3 Xrotation Yrotation Zrotation
+			JOINT def_c_tail2_joint
+			{
+				OFFSET -0.000000 3.790200 -2.331700
+				CHANNELS 3 Xrotation Yrotation Zrotation
+				End Site
+				{
+					OFFSET 0.000000 3.790200 -2.331700
+				}
+			}
+		}
+		JOINT def_rearLegUpr_joint.L
+		{
+			OFFSET 2.782800 2.468600 -0.676100
+			CHANNELS 3 Xrotation Yrotation Zrotation
+			JOINT def_rearLegLwr_joint.L
+			{
+				OFFSET 0.580500 0.355400 -7.589500
+				CHANNELS 3 Xrotation Yrotation Zrotation
+				JOINT def_rearHorselink_joint.L
+				{
+					OFFSET 0.154800 4.939300 -5.051700
+					CHANNELS 3 Xrotation Yrotation Zrotation
+					JOINT def_rearFoot_joint.L
+					{
+						OFFSET 0.508000 -1.094400 -5.763100
+						CHANNELS 3 Xrotation Yrotation Zrotation
+						End Site
+						{
+							OFFSET 0.091375 -0.447450 -0.503600
+						}
+					}
+				}
+				JOINT def_rearLegLwrAllTwist_joint.L
+				{
+					OFFSET 0.077400 2.469700 -2.525800
+					CHANNELS 3 Xrotation Yrotation Zrotation
+					End Site
+					{
+						OFFSET 0.046400 1.481800 -1.515500
+					}
+				}
+			}
+			JOINT def_rearLegUprAllTwist_joint.L
+			{
+				OFFSET 0.290200 0.177700 -3.794700
+				CHANNELS 3 Xrotation Yrotation Zrotation
+				End Site
+				{
+					OFFSET 0.174100 0.106600 -2.276800
+				}
+			}
+		}
+		JOINT def_rearLegUpr_joint.R
+		{
+			OFFSET -2.782800 2.468600 -0.676000
+			CHANNELS 3 Xrotation Yrotation Zrotation
+			JOINT def_rearLegLwr_joint.R
+			{
+				OFFSET -0.580900 0.347100 -7.589900
+				CHANNELS 3 Xrotation Yrotation Zrotation
+				JOINT def_rearHorselink_joint.R
+				{
+					OFFSET -0.154300 4.947600 -5.051400
+					CHANNELS 3 Xrotation Yrotation Zrotation
+					JOINT def_rearFoot_joint.R
+					{
+						OFFSET -0.508000 -1.094400 -5.763100
+						CHANNELS 3 Xrotation Yrotation Zrotation
+						End Site
+						{
+							OFFSET -0.091350 -0.447425 -0.503600
+						}
+					}
+				}
+				JOINT def_rearLegLwrAllTwist_joint.R
+				{
+					OFFSET -0.089700 2.369900 -2.619300
+					CHANNELS 3 Xrotation Yrotation Zrotation
+					End Site
+					{
+						OFFSET -0.053800 1.423000 -1.572900
+					}
+				}
+			}
+			JOINT def_rearLegUprAllTwist_joint.R
+			{
+				OFFSET -0.290500 0.173500 -3.794900
+				CHANNELS 3 Xrotation Yrotation Zrotation
+				End Site
+				{
+					OFFSET -0.174300 0.104100 -2.277000
+				}
+			}
+		}
+		JOINT def_c_anus_joint
+		{
+			OFFSET -0.000000 7.183100 -2.297300
+			CHANNELS 3 Xrotation Yrotation Zrotation
+			End Site
+			{
+				OFFSET 0.000000 0.936100 -0.713600
+			}
+		}
+	}
+}
+MOTION
+Frames: 49
+Frame Time: 0.033333
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