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>

third_party/Dyn-HaMR/_DATA/BMC

@@ -0,0 +1 @@
+../dyn-hamr/optim/BMC

third_party/Dyn-HaMR/_DATA/data/mano

@@ -0,0 +1 @@
+../../../hamer/_DATA/data/mano

third_party/Dyn-HaMR/_DATA/data/mano_mean_params.npz

@@ -0,0 +1 @@
+../../../hamer/_DATA/data/mano_mean_params.npz

third_party/Dyn-HaMR/_DATA/hamer_ckpts/checkpoints

@@ -0,0 +1 @@
+../../../hamer/_DATA/hamer_ckpts/checkpoints

third_party/Dyn-HaMR/_DATA/hamer_ckpts/dataset_config.yaml

@@ -0,0 +1,42 @@
+COCOW-TRAIN:
+  TYPE: ImageDataset
+  URLS: hamer_training_data/dataset_tars/cocow-train/{000000..000036}.tar
+  epoch_size: 78666
+DEX-TRAIN:
+  TYPE: ImageDataset
+  URLS: hamer_training_data/dataset_tars/dex-train/{000000..000406}.tar
+  epoch_size: 406888
+FREIHAND-MOCAP:
+  DATASET_FILE: hamer_training_data/freihand_mocap.npz
+FREIHAND-TRAIN:
+  TYPE: ImageDataset
+  URLS: hamer_training_data/dataset_tars/freihand-train/{000000..000130}.tar
+  epoch_size: 130240
+H2O3D-TRAIN:
+  TYPE: ImageDataset
+  URLS: hamer_training_data/dataset_tars/h2o3d-train/{000000..000060}.tar
+  epoch_size: 121996
+HALPE-TRAIN:
+  TYPE: ImageDataset
+  URLS: hamer_training_data/dataset_tars/halpe-train/{000000..000022}.tar
+  epoch_size: 34289
+HO3D-TRAIN:
+  TYPE: ImageDataset
+  URLS: hamer_training_data/dataset_tars/ho3d-train/{000000..000083}.tar
+  epoch_size: 83325
+INTERHAND26M-TRAIN:
+  TYPE: ImageDataset
+  URLS: hamer_training_data/dataset_tars/interhand26m-train/{000000..001056}.tar
+  epoch_size: 1424632
+MPIINZSL-TRAIN:
+  TYPE: ImageDataset
+  URLS: hamer_training_data/dataset_tars/mpiinzsl-train/{000000..000015}.tar
+  epoch_size: 15184
+MTC-TRAIN:
+  TYPE: ImageDataset
+  URLS: hamer_training_data/dataset_tars/mtc-train/{000000..000306}.tar
+  epoch_size: 363947
+RHD-TRAIN:
+  TYPE: ImageDataset
+  URLS: hamer_training_data/dataset_tars/rhd-train/{000000..000041}.tar
+  epoch_size: 61705

third_party/Dyn-HaMR/_DATA/hamer_ckpts/model_config.yaml

@@ -0,0 +1,111 @@
+task_name: train
+tags:
+- dev
+train: true
+test: false
+ckpt_path: null
+seed: null
+DATASETS:
+  TRAIN:
+    FREIHAND-TRAIN:
+      WEIGHT: 0.25
+    INTERHAND26M-TRAIN:
+      WEIGHT: 0.25
+    MTC-TRAIN:
+      WEIGHT: 0.1
+    RHD-TRAIN:
+      WEIGHT: 0.05
+    COCOW-TRAIN:
+      WEIGHT: 0.1
+    HALPE-TRAIN:
+      WEIGHT: 0.05
+    MPIINZSL-TRAIN:
+      WEIGHT: 0.05
+    HO3D-TRAIN:
+      WEIGHT: 0.05
+    H2O3D-TRAIN:
+      WEIGHT: 0.05
+    DEX-TRAIN:
+      WEIGHT: 0.05
+  VAL:
+    FREIHAND-TRAIN:
+      WEIGHT: 1.0
+  MOCAP: FREIHAND-MOCAP
+  BETAS_REG: true
+  CONFIG:
+    SCALE_FACTOR: 0.3
+    ROT_FACTOR: 30
+    TRANS_FACTOR: 0.02
+    COLOR_SCALE: 0.2
+    ROT_AUG_RATE: 0.6
+    TRANS_AUG_RATE: 0.5
+    DO_FLIP: false
+    FLIP_AUG_RATE: 0.0
+    EXTREME_CROP_AUG_RATE: 0.0
+    EXTREME_CROP_AUG_LEVEL: 1
+extras:
+  ignore_warnings: false
+  enforce_tags: true
+  print_config: true
+exp_name: hamer
+MANO:
+  DATA_DIR: _DATA/data/
+  MODEL_PATH: data/mano
+  GENDER: neutral
+  NUM_HAND_JOINTS: 15
+  MEAN_PARAMS: data/mano_mean_params.npz
+  CREATE_BODY_POSE: false
+EXTRA:
+  FOCAL_LENGTH: 5000
+  NUM_LOG_IMAGES: 4
+  NUM_LOG_SAMPLES_PER_IMAGE: 8
+  PELVIS_IND: 0
+GENERAL:
+  TOTAL_STEPS: 1000000
+  LOG_STEPS: 1000
+  VAL_STEPS: 1000
+  CHECKPOINT_STEPS: 10000
+  CHECKPOINT_SAVE_TOP_K: 1
+  NUM_WORKERS: 8
+  PREFETCH_FACTOR: 2
+TRAIN:
+  LR: 1.0e-05
+  WEIGHT_DECAY: 0.0001
+  BATCH_SIZE: 32
+  LOSS_REDUCTION: mean
+  NUM_TRAIN_SAMPLES: 2
+  NUM_TEST_SAMPLES: 64
+  POSE_2D_NOISE_RATIO: 0.01
+  SMPL_PARAM_NOISE_RATIO: 0.005
+MODEL:
+  IMAGE_SIZE: 256
+  IMAGE_MEAN:
+  - 0.485
+  - 0.456
+  - 0.406
+  IMAGE_STD:
+  - 0.229
+  - 0.224
+  - 0.225
+  BACKBONE:
+    TYPE: vit
+    PRETRAINED_WEIGHTS: hamer_training_data/vitpose_backbone.pth
+  MANO_HEAD:
+    TYPE: transformer_decoder
+    IN_CHANNELS: 2048
+    TRANSFORMER_DECODER:
+      depth: 6
+      heads: 8
+      mlp_dim: 1024
+      dim_head: 64
+      dropout: 0.0
+      emb_dropout: 0.0
+      norm: layer
+      context_dim: 1280
+LOSS_WEIGHTS:
+  KEYPOINTS_3D: 0.05
+  KEYPOINTS_2D: 0.01
+  GLOBAL_ORIENT: 0.001
+  HAND_POSE: 0.001
+  BETAS: 0.0005
+  ADVERSARIAL: 0.0005

third_party/Dyn-HaMR/dyn-hamr/vis/tools.py

@@ -1037,6 +1037,11 @@ def batch_rodrigues(param):
 def smooth_global_rot_matrix(pred_rots, OE_filter):
     rot_mat = batch_rodrigues(pred_rots[None]).squeeze(0)
     smoothed_rot_mat = OE_filter.process(rot_mat)
+    # Elementwise filtering leaves SO(3); project back before axis-angle conversion.
+    u, _, vh = torch.linalg.svd(smoothed_rot_mat)
+    correction = torch.eye(3, dtype=u.dtype, device=u.device)
+    correction[-1, -1] = torch.det(u @ vh)
+    smoothed_rot_mat = u @ correction @ vh
     smoothed_rot = rotation_matrix_to_angle_axis(smoothed_rot_mat.reshape(1,3,3)).reshape(-1)
     return smoothed_rot
 
@@ -1106,4 +1111,4 @@ class OneEuroFilter:
         cutoff = self.mincutoff + self.beta * torch.abs(edx)
     if print_inter:
         print(self.compute_alpha(cutoff))
-    return self.x_filter.process(x, self.compute_alpha(cutoff))
+    return self.x_filter.process(x, self.compute_alpha(cutoff))

third_party/Dyn-HaMR/dyn-hamr/vis/viewer.py

@@ -177,6 +177,8 @@ class AnimationBase(object):
         """
         Add a single camera marker node that we move around when we animate
         """
+        if os.environ.get("DYNHAMR_HIDE_CAMERA_MARKERS") == "1":
+            return
         print("ADDING CAMERA MARKERS")
         if self.cam_marker_node is None:
             cam_marker = make_camera_marker(up="y")

third_party/Dyn-HaMR/third-party/hamer/pretrained_models/detector.pt

@@ -0,0 +1 @@
+../../../../../weights/detector.pt