Update to 2026-09-17 pipeline snapshot; add weights, L20 assets and recording via Git LFS

Source: RGB-D -> Dyn-HaMR -> L20 retargeting -> FoundationPose -> reference repair -> SPIDER,
documented in docs/PIPELINE_LATEST.md and docs/SETUP_AND_WEIGHTS.md. Adds FoundationPose and
nvdiffrast upstream snapshots, requirements/pipeline_venv.txt and the FoundationPose weight
manifest/downloader.

Assets (Git LFS): weights/ (WiLoR detector, HandFlow denoiser, UniDepth-L), FoundationPose
checkpoints, HaMeR checkpoint, Dyn-HaMR HMP model and BMC constraints, L20 URDF/meshes, the
20260915_171525 D405 recording and the two box CADs. MANO models are not redistributed
(third_party/hamer/_DATA/data/mano/README.txt). Environments, caches and run outputs excluded.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

scripts/run_dynhamr_rgbd_20260915.py

@@ -0,0 +1,49 @@
+"""Run both-hand Dyn-HaMR with measured intrinsics and RGB-D odometry."""
+import json
+import os
+from pathlib import Path
+import subprocess
+import time
+import numpy as np
+
+ROOT = Path(__file__).resolve().parents[1]
+OUT = ROOT / 'output/20260915_171525_dynhamr'
+OUT.mkdir(parents=True, exist_ok=True)
+SEQ = '20260915_171525'
+dataset = OUT / 'dataset'
+(dataset / 'videos').mkdir(parents=True, exist_ok=True)
+video = dataset / 'videos' / (SEQ + '.mp4')
+if not video.exists():
+    video.symlink_to(ROOT / 'docs' / SEQ / 'color.mp4')
+camera = np.load(ROOT / 'output' / SEQ / 'rgbd_camera.npz')
+assert camera['c2w'].shape == (352, 4, 4)
+for kind, data in [('pose', camera['c2w']), ('intrinsics', np.tile(camera['intrinsics'], (352, 1)))]:
+    directory = OUT / 'rgbd_cameras' / kind
+    directory.mkdir(parents=True, exist_ok=True)
+    np.savez(directory / (SEQ + '.npz'), data=data.astype(np.float32), inds=np.arange(352))
+(OUT / 'camera_provenance.json').write_text(json.dumps(dict(
+    source=str(ROOT / 'output' / SEQ / 'rgbd_camera.npz'),
+    method=str(camera['method']), adapter='ViPE file schema only; cameras estimated from D405 RGB-D',
+    hand_reconstruction='Dyn-HaMR', hands=['left', 'right']), indent=2))
+dyn = ROOT / 'third_party/Dyn-HaMR'
+env = os.environ.copy()
+env.update(TORCH_HOME=str(ROOT / '.torch_cache'), HF_HOME=str(ROOT / '.hf_cache'),
+           HF_HUB_OFFLINE='1', TRANSFORMERS_OFFLINE='1', PYTHONUNBUFFERED='1',
+           MPLCONFIGDIR='/tmp/matplotlib-dynhamr', HYDRA_FULL_ERROR='1',
+           PYOPENGL_PLATFORM='egl', LD_LIBRARY_PATH='')
+env['PATH'] = str(dyn / '.dynhamr/bin') + ':' + env['PATH']
+env['PYTHONPATH'] = ':'.join(str(dyn / p) for p in ['dyn-hamr', 'third-party/hamer', 'third-party/hamer/third-party/ViTPose'])
+cmd = [str(dyn / '.dynhamr/bin/python'), '-u', 'run_opt.py', 'data=video_vipe',
+       'data.root=' + str(dataset), 'data.seq="' + SEQ + '"',
+       'data.vipe_dir=' + str(OUT / 'rgbd_cameras'), 'data.frame_opts.fps=30',
+       'is_static=False', 'model.opt_scale=False', 'run_opt=True', 'run_vis=True',
+       'run_prior=True', '+data.vipe_pipeline=dynhamr_cameras',
+       'HMP.vid_path=' + str(video), 'hydra.run.dir=' + str(OUT / 'optimization')]
+def status(stage, **kw):
+    (OUT / 'status.json').write_text(json.dumps(dict(stage=stage,
+        updated=time.strftime('%Y-%m-%d %H:%M:%S'), pid=os.getpid(), **kw), indent=2))
+status('RUNNING', command=cmd)
+with (OUT / 'run.log').open('a') as log:
+    result = subprocess.run(cmd, cwd=dyn / 'dyn-hamr', env=env, stdout=log, stderr=subprocess.STDOUT)
+status('RECONSTRUCTION_PROCESS_EXITED' if result.returncode == 0 else 'FAILED', returncode=result.returncode)
+raise SystemExit(result.returncode)