Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-09-15 14:13:49 +08:00
commit 7e4ef6f98b
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+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from torch.nn import functional as F
+
+
+def rot6d_to_rotmat(x):
+    """Convert 6D rotation representation to 3x3 rotation matrix.
+
+    Based on Zhou et al., "On the Continuity of Rotation
+    Representations in Neural Networks", CVPR 2019
+    Input:
+        (B,6) Batch of 6-D rotation representations
+    Output:
+        (B,3,3) Batch of corresponding rotation matrices
+    """
+    x = x.view(-1, 3, 2)
+    a1 = x[:, :, 0]
+    a2 = x[:, :, 1]
+    b1 = F.normalize(a1)
+    b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1)
+    b3 = torch.cross(b1, b2)
+    return torch.stack((b1, b2, b3), dim=-1)
+
+
+def batch_rodrigues(theta):
+    """Convert axis-angle representation to rotation matrix.
+    Args:
+        theta: size = [B, 3]
+    Returns:
+        Rotation matrix corresponding to the quaternion
+            -- size = [B, 3, 3]
+    """
+    l2norm = torch.norm(theta + 1e-8, p=2, dim=1)
+    angle = torch.unsqueeze(l2norm, -1)
+    normalized = torch.div(theta, angle)
+    angle = angle * 0.5
+    v_cos = torch.cos(angle)
+    v_sin = torch.sin(angle)
+    quat = torch.cat([v_cos, v_sin * normalized], dim=1)
+    return quat_to_rotmat(quat)
+
+
+def quat_to_rotmat(quat):
+    """Convert quaternion coefficients to rotation matrix.
+    Args:
+        quat: size = [B, 4] 4 <===>(w, x, y, z)
+    Returns:
+        Rotation matrix corresponding to the quaternion
+            -- size = [B, 3, 3]
+    """
+    norm_quat = quat
+    norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True)
+    w, x, y, z = norm_quat[:, 0], norm_quat[:, 1],\
+        norm_quat[:, 2], norm_quat[:, 3]
+
+    B = quat.size(0)
+
+    w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
+    wx, wy, wz = w * x, w * y, w * z
+    xy, xz, yz = x * y, x * z, y * z
+
+    rotMat = torch.stack([
+        w2 + x2 - y2 - z2, 2 * xy - 2 * wz, 2 * wy + 2 * xz, 2 * wz + 2 * xy,
+        w2 - x2 + y2 - z2, 2 * yz - 2 * wx, 2 * xz - 2 * wy, 2 * wx + 2 * yz,
+        w2 - x2 - y2 + z2
+    ],
+                         dim=1).view(B, 3, 3)
+    return rotMat