obj for download, glb for visualization

apps/infer.py

@@ -296,6 +296,8 @@ def generate_model(in_path, model_type):
         smpl_obj.visual.vertex_colors = (smpl_obj.vertex_normals+1.0)*255.0*0.5
         smpl_obj.export(
             f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.obj")
+        smpl_obj.export(
+            f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.glb")
 
         smpl_info = {'betas': optimed_betas,
                      'pose': optimed_pose,
@@ -338,6 +340,10 @@ def generate_model(in_path, model_type):
             os.path.join(config_dict['out_dir'], cfg.name,
                          f"obj/{data['name']}_recon.obj")
         )
+        recon_obj.export(
+            os.path.join(config_dict['out_dir'], cfg.name,
+                         f"obj/{data['name']}_recon.glb")
+        )
         
         # Isotropic Explicit Remeshing for better geometry topology
         verts_refine, faces_refine = remesh(os.path.join(config_dict['out_dir'], cfg.name,
@@ -419,6 +425,8 @@ def generate_model(in_path, model_type):
             final.visual.vertex_colors = final_colors
             final.export(
                 f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_refine.obj")
+            final.export(
+                f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_refine.glb")
 
 
         # always export visualized video regardless of the cloth refinment
@@ -438,10 +446,13 @@ def generate_model(in_path, model_type):
                          f"vid/{data['name']}_cloth.mp4"),
         )
     
-    smpl_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.obj"
+    smpl_obj_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.obj"
+    smpl_glb_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.glb"
     smpl_npy_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_smpl.npy"
-    recon_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_recon.obj"
-    refine_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_refine.obj"
+    recon_obj_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_recon.obj"
+    recon_glb_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_recon.glb"
+    refine_obj_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_refine.obj"
+    refine_glb_path = f"{config_dict['out_dir']}/{cfg.name}/obj/{data['name']}_refine.glb"
     
     video_path = os.path.join(config_dict['out_dir'], cfg.name, f"vid/{data['name']}_cloth.mp4")
     overlap_path = os.path.join(config_dict['out_dir'], cfg.name, f"png/{data['name']}_overlap.png")
@@ -453,4 +464,8 @@ def generate_model(in_path, model_type):
     gc.collect()
     torch.cuda.empty_cache()
     
-    return [smpl_path, smpl_path, smpl_npy_path, recon_path, recon_path, refine_path, refine_path, video_path, video_path, overlap_path]
+    return [smpl_glb_path, smpl_obj_path, 
+            smpl_npy_path, 
+            recon_glb_path, recon_obj_path, 
+            refine_glb_path, refine_obj_path, 
+            video_path, video_path, overlap_path]