feat: clothing lane, character sources, and DCC bridges

Bulk import of the working lanes that were living untracked on the PC.

Content:
- characters/  Lena/male body lanes, bakes, texture work, run logs
- clothing/    garment pipeline, configs, gates, contract docs
- garments/    MD-authored garment sources (.zprj/.zpac)
- UAL-Lib/     Universal Animation Library 2 source (.blend/.fbx/.glb)
- tools/       blender_bridge, iclone_bridge, md_bridge, tailor, glm_agent
- docs/, plans/, dev/, .agents/plans/

Repo hygiene:
- .gitattributes: LFS now covers .blend, .zprj, .zpac, .obj, .npy and the
  Reallusion .iAvatar/.ccAvatar/.ccRestore containers. Without this the
  ~3.8 GB in this commit would land as raw blobs. .png/.jpg are left out
  on purpose — ~250 are already tracked raw and converting them would
  rewrite every one without shrinking history.
- .gitignore: exclude /accurig/ (~1 GB AccuRig program files, redistributable
  from Reallusion, nothing authored here) and /dev/null/ (git-lfs hook copies
  dropped by a `>/dev/null` redirect on Windows).

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

characters/female/lena_nude/hires_claude/35_silhouette.py

@@ -0,0 +1,77 @@
+# Stage 35 (read-only): does the approved concept turnaround describe OUR body?
+#
+#   blender --background --python 35_silhouette.py -- <concept_front.png> <our_front.png>
+#
+# This decides how the turnaround can be used. If the silhouettes match, the concept renders could
+# be projected onto the mesh as a texture source. If they do not, they are a style and tone
+# reference only — and quietly projecting them would smear one body's paint onto another's shape.
+# Widths are normalised by figure height, so framing and resolution cancel.
+import bpy, sys, os
+import numpy as np
+
+argv = sys.argv[sys.argv.index("--") + 1:]
+
+
+def load(p):
+    im = bpy.data.images.load(p)
+    w, h = im.size
+    b = np.empty(w * h * 4, dtype=np.float32)
+    im.pixels.foreach_get(b)
+    a = b.reshape(h, w, 4)[:, :, :3].copy()
+    bpy.data.images.remove(im)
+    return a[::-1]
+
+
+def profile(p):
+    A = load(p)
+    sat = A.max(axis=2) - A.min(axis=2)
+    m = (sat > 0.06) & (A.max(axis=2) > 0.15)
+    ys, xs = np.nonzero(m)
+    y0, y1 = ys.min(), ys.max()
+    Hf = float(y1 - y0)
+    cx = xs.mean()
+    out = []
+    for t in np.arange(0.02, 1.0, 0.02):
+        r = int(round(y0 + t * Hf))
+        row = np.nonzero(m[r])[0]
+        if len(row) < 2:
+            out.append((t, np.nan, np.nan))
+            continue
+        # full extent (includes arms where they are out), and the CENTRAL run (the torso/leg)
+        full = (row.max() - row.min()) / Hf
+        # central run: walk out from the pixel nearest the body's x centre
+        k = row[np.argmin(np.abs(row - cx))]
+        lo = k
+        while lo - 1 in set(row.tolist()) if False else (lo - 1 >= 0 and m[r, lo - 1]):
+            lo -= 1
+        hi = k
+        while hi + 1 < m.shape[1] and m[r, hi + 1]:
+            hi += 1
+        out.append((t, full, (hi - lo) / Hf))
+    return np.array(out), Hf
+
+
+A, ha = profile(argv[0])
+B, hb = profile(argv[1])
+print(f"concept figure height {ha:.0f} px   ours {hb:.0f} px")
+print("\n  t     concept_full  ours_full   concept_core  ours_core   core_diff")
+diffs = []
+for (t, fa, ca), (_, fb, cb) in zip(A, B):
+    if np.isnan(ca) or np.isnan(cb):
+        continue
+    d = cb - ca
+    # the central run is only meaningful where it is a single body part:
+    # below the arms (t>0.30) and above the ankles
+    flag = ""
+    if 0.30 < t < 0.95:
+        diffs.append(d)
+        if abs(d) > 0.02:
+            flag = "  <-- differs"
+    print(f" {t:.2f}   {fa:9.3f}   {fb:9.3f}   {ca:10.3f}  {cb:9.3f}   {d:+8.3f}{flag}")
+if diffs:
+    diffs = np.array(diffs)
+    print(f"\nCORE WIDTH (torso/legs, t 0.30-0.95), ours minus concept, as fraction of height:")
+    print(f"  mean {diffs.mean():+.4f}   mean|d| {np.abs(diffs).mean():.4f}   "
+          f"max|d| {np.abs(diffs).max():.4f}")
+    print(f"  bands differing by >2% of height: {int((np.abs(diffs)>0.02).sum())} of {len(diffs)}")
+print("SILHOUETTE_DONE")