diff --git a/models/cvae_model.py b/models/cvae_model.py
index d71c651..b81ee6a 100644
--- a/models/cvae_model.py
+++ b/models/cvae_model.py
@@ -99,8 +99,8 @@ class CVAE(nn.Module):
         z_body = self.body_enc(uv_body_posed)
         z_static = self.static_enc(uv_template)
         if self.use_normals:
-            z_curv = self.norm_enc(uv_curv)
-            z_cond = torch.concat([z_static, z_body, z_curv], dim=1)
+            z_norm = self.norm_enc(uv_curv)
+            z_cond = torch.concat([z_static, z_body, z_norm], dim=1)
         else:
             z_cond = torch.concat([z_static, z_body], dim=1)
 
@@ -108,13 +108,13 @@ class CVAE(nn.Module):
 
 
     def forward(self, inputs):
-        uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask = inputs
-        x = torch.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], dim=1)
+        uv_unposed, uv_template, uv_body_posed, uv_norm, uv_mask = inputs
+        x = torch.concat([uv_unposed, uv_template, uv_body_posed, uv_norm], dim=1)
         
         mean, logvar = self.encoder(x)
         z: torch.Tensor = self.reparameterize(mean, logvar)
         z = z.view((mean.shape[0], *self.latent_shape))
-        z_cond = self.encode_conds(uv_template, uv_body_posed, uv_curv)
+        z_cond = self.encode_conds(uv_template, uv_body_posed, uv_norm)
 
         z = torch.concat([z, z_cond], dim=1)
         x = self.decoder(z)