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

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

third_party/Dyn-HaMR/third-party/DROID-SLAM/droid_slam/modules/gru.py

@@ -0,0 +1,34 @@
+import torch
+import torch.nn as nn
+
+
+class ConvGRU(nn.Module):
+    def __init__(self, h_planes=128, i_planes=128):
+        super(ConvGRU, self).__init__()
+        self.do_checkpoint = False
+        self.convz = nn.Conv2d(h_planes+i_planes, h_planes, 3, padding=1)
+        self.convr = nn.Conv2d(h_planes+i_planes, h_planes, 3, padding=1)
+        self.convq = nn.Conv2d(h_planes+i_planes, h_planes, 3, padding=1)
+
+        self.w = nn.Conv2d(h_planes, h_planes, 1, padding=0)
+
+        self.convz_glo = nn.Conv2d(h_planes, h_planes, 1, padding=0)
+        self.convr_glo = nn.Conv2d(h_planes, h_planes, 1, padding=0)
+        self.convq_glo = nn.Conv2d(h_planes, h_planes, 1, padding=0)
+
+    def forward(self, net, *inputs):
+        inp = torch.cat(inputs, dim=1)
+        net_inp = torch.cat([net, inp], dim=1)
+
+        b, c, h, w = net.shape
+        glo = torch.sigmoid(self.w(net)) * net
+        glo = glo.view(b, c, h*w).mean(-1).view(b, c, 1, 1)
+
+        z = torch.sigmoid(self.convz(net_inp) + self.convz_glo(glo))
+        r = torch.sigmoid(self.convr(net_inp) + self.convr_glo(glo))
+        q = torch.tanh(self.convq(torch.cat([r*net, inp], dim=1)) + self.convq_glo(glo))
+
+        net = (1-z) * net + z * q
+        return net
+
+