Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,11 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+logs/0d6e37451ce97b5e97dd74e63f9a06b17644f2ad02f32534ce079493808d288f.glb filter=lfs diff=lfs merge=lfs -text
+logs/_tmp_gradio_312f2981ccbecf05b94e9729c5e88e720cafa46c_1e5133dcc83642c805644360a403f7c38a9dffc7f860cb96a678c76708d997c0.glb filter=lfs diff=lfs merge=lfs -text
+logs/_tmp_gradio_6a883f05b5f52ebd382b11a3aef0825b272d0837_2f78ca4365556592bd54881407d40b3928870cfa6d89ec389b65356b3759c96c.glb filter=lfs diff=lfs merge=lfs -text
+logs/tiger_rgba.glb filter=lfs diff=lfs merge=lfs -text
+src/demo/logs/0d6e37451ce97b5e97dd74e63f9a06b17644f2ad02f32534ce079493808d288f.glb filter=lfs diff=lfs merge=lfs -text
+src/demo/logs/_tmp_gradio_312f2981ccbecf05b94e9729c5e88e720cafa46c_1e5133dcc83642c805644360a403f7c38a9dffc7f860cb96a678c76708d997c0.glb filter=lfs diff=lfs merge=lfs -text
+src/demo/logs/_tmp_gradio_6a883f05b5f52ebd382b11a3aef0825b272d0837_2f78ca4365556592bd54881407d40b3928870cfa6d89ec389b65356b3759c96c.glb filter=lfs diff=lfs merge=lfs -text
+src/demo/logs/tiger_rgba.glb filter=lfs diff=lfs merge=lfs -text

README.md

@@ -26,17 +26,73 @@ pip install gradio_model4dgs
 import gradio as gr
 from gradio_model4dgs import Model4DGS
 import os
+from PIL import Image
+import hashlib
 
-image_dir = os.path.join(os.path.dirname(__file__), "assets")
+def check_img_input(control_image):
+    if control_image is None:
+        raise gr.Error("Please select or upload an input image")
 
-if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
-    examples = [os.path.join(image_dir, file) for file in os.listdir(image_dir)]
-else:
-    examples = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        Model4DGS(value=examples, label="4D Model")
+if __name__ == "__main__":
+    _TITLE = '''DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation'''
+
+    _DESCRIPTION = '''
+    <div>
+    <a style="display:inline-block" href="https://jiawei-ren.github.io/projects/dreamgaussian4d/"><img src='https://img.shields.io/badge/public_website-8A2BE2'></a>
+    <a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2312.17142"><img src="https://img.shields.io/badge/2309.16653-f9f7f7?logo=data:image/png;base64,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"></a>
+    <a style="display:inline-block; margin-left: .5em" href='https://github.com/jiawei-ren/dreamgaussian4d'><img src='https://img.shields.io/github/stars/jiawei-ren/dreamgaussian4d?style=social'/></a>
+    </div>
+    We introduce DreamGaussian4D, an efficient 4D generation framework that builds on 4D Gaussian Splatting representation.
+    '''
+
+    # load images in 'assets' folder as examples
+    image_dir = os.path.join(os.path.dirname(__file__), "assets")
+    examples_img = None
+
+    if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
+        examples_4d = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.ply')]
+        examples_img = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.png')]
+    else:
+        examples_4d = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
+        
+    def optimize(image_block: Image.Image):
+        # temporarily only show tiger
+        return f'{os.path.join(os.path.dirname(__file__), "logs")}/tiger.glb', examples_4d
+
+    # Compose demo layout & data flow
+    with gr.Blocks(title=_TITLE, theme=gr.themes.Soft()) as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown('# ' + _TITLE)
+        gr.Markdown(_DESCRIPTION)
+
+        with gr.Row(variant='panel'):
+            left_column = gr.Column(scale=5)
+            with left_column:
+                image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Input image')
+
+                preprocess_chk = gr.Checkbox(True,
+                                             label='Preprocess image automatically (remove background and recenter object)')
+
+            with gr.Column(scale=5):
+                obj3d = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model (Final)")
+                obj4d = Model4DGS(label="4D Model")
+
+            with left_column:
+                gr.Examples(
+                    examples=examples_img,  # NOTE: elements must match inputs list!
+                    inputs=image_block,
+                    outputs=obj3d,
+                    fn=optimize,
+                    label='Examples (click one of the images below to start)',
+                    examples_per_page=40
+                )
+                img_run_btn = gr.Button("Generate 4D")
+
+            # if there is an input image, continue with inference
+            # else display an error message
+            img_run_btn.click(check_img_input, inputs=[image_block], queue=False).success(
+                optimize, inputs=[image_block], outputs=[obj3d, obj4d])
 
 if __name__ == "__main__":
     demo.launch(share=True)

app.py

@@ -1,17 +1,73 @@
 import gradio as gr
 from gradio_model4dgs import Model4DGS
 import os
+from PIL import Image
+import hashlib
 
-image_dir = os.path.join(os.path.dirname(__file__), "assets")
+def check_img_input(control_image):
+    if control_image is None:
+        raise gr.Error("Please select or upload an input image")
 
-if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
-    examples = [os.path.join(image_dir, file) for file in os.listdir(image_dir)]
-else:
-    examples = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
+if __name__ == "__main__":
+    _TITLE = '''DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation'''
+
+    _DESCRIPTION = '''
+    <div>
+    <a style="display:inline-block" href="https://jiawei-ren.github.io/projects/dreamgaussian4d/"><img src='https://img.shields.io/badge/public_website-8A2BE2'></a>
+    <a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2312.17142"><img src="https://img.shields.io/badge/2309.16653-f9f7f7?logo=data:image/png;base64,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"></a>
+    <a style="display:inline-block; margin-left: .5em" href='https://github.com/jiawei-ren/dreamgaussian4d'><img src='https://img.shields.io/github/stars/jiawei-ren/dreamgaussian4d?style=social'/></a>
+    </div>
+    We introduce DreamGaussian4D, an efficient 4D generation framework that builds on 4D Gaussian Splatting representation.
+    '''
+
+    # load images in 'assets' folder as examples
+    image_dir = os.path.join(os.path.dirname(__file__), "assets")
+    examples_img = None
+
+    if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
+        examples_4d = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.ply')]
+        examples_img = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.png')]
+    else:
+        examples_4d = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
+        
+    def optimize(image_block: Image.Image):
+        # temporarily only show tiger
+        return f'{os.path.join(os.path.dirname(__file__), "logs")}/tiger.glb', examples_4d
+
+    # Compose demo layout & data flow
+    with gr.Blocks(title=_TITLE, theme=gr.themes.Soft()) as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown('# ' + _TITLE)
+        gr.Markdown(_DESCRIPTION)
+
+        with gr.Row(variant='panel'):
+            left_column = gr.Column(scale=5)
+            with left_column:
+                image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Input image')
+
+                preprocess_chk = gr.Checkbox(True,
+                                             label='Preprocess image automatically (remove background and recenter object)')
+
+            with gr.Column(scale=5):
+                obj3d = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model (Final)")
+                obj4d = Model4DGS(label="4D Model")
+
+            with left_column:
+                gr.Examples(
+                    examples=examples_img,  # NOTE: elements must match inputs list!
+                    inputs=image_block,
+                    outputs=obj3d,
+                    fn=optimize,
+                    label='Examples (click one of the images below to start)',
+                    examples_per_page=40
+                )
+                img_run_btn = gr.Button("Generate 4D")
 
-with gr.Blocks() as demo:
-    with gr.Row():
-        Model4DGS(value=examples, label="4D Model")
+            # if there is an input image, continue with inference
+            # else display an error message
+            img_run_btn.click(check_img_input, inputs=[image_block], queue=False).success(
+                optimize, inputs=[image_block], outputs=[obj3d, obj4d])
 
 if __name__ == "__main__":
     demo.launch(share=True)

logs/0d6e37451ce97b5e97dd74e63f9a06b17644f2ad02f32534ce079493808d288f.glb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36c0b461d6973a61b97cfdcffb49c1e6e2ac2730cda3da2a53ca18eb1300c21d
+size 2101240

logs/_tmp_gradio_312f2981ccbecf05b94e9729c5e88e720cafa46c_1e5133dcc83642c805644360a403f7c38a9dffc7f860cb96a678c76708d997c0.glb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f898caa81d1c4eebae1bd8a3dd0581fd787290abc7c174051c0ab0366bb231f
+size 2614792

logs/_tmp_gradio_6a883f05b5f52ebd382b11a3aef0825b272d0837_2f78ca4365556592bd54881407d40b3928870cfa6d89ec389b65356b3759c96c.glb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f4b8d60a1208343c984b7bb129efb0a175b7f7dddd737e98e0302f4931f6dda
+size 2711984

logs/tiger_rgba.glb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f898caa81d1c4eebae1bd8a3dd0581fd787290abc7c174051c0ab0366bb231f
+size 2614792

space.py

@@ -41,17 +41,73 @@ pip install gradio_model4dgs
 import gradio as gr
 from gradio_model4dgs import Model4DGS
 import os
+from PIL import Image
+import hashlib
 
-image_dir = os.path.join(os.path.dirname(__file__), "assets")
+def check_img_input(control_image):
+    if control_image is None:
+        raise gr.Error("Please select or upload an input image")
 
-if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
-    examples = [os.path.join(image_dir, file) for file in os.listdir(image_dir)]
-else:
-    examples = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        Model4DGS(value=examples, label="4D Model")
+if __name__ == "__main__":
+    _TITLE = '''DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation'''
+
+    _DESCRIPTION = '''
+    <div>
+    <a style="display:inline-block" href="https://jiawei-ren.github.io/projects/dreamgaussian4d/"><img src='https://img.shields.io/badge/public_website-8A2BE2'></a>
+    <a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2312.17142"><img src="https://img.shields.io/badge/2309.16653-f9f7f7?logo=data:image/png;base64,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"></a>
+    <a style="display:inline-block; margin-left: .5em" href='https://github.com/jiawei-ren/dreamgaussian4d'><img src='https://img.shields.io/github/stars/jiawei-ren/dreamgaussian4d?style=social'/></a>
+    </div>
+    We introduce DreamGaussian4D, an efficient 4D generation framework that builds on 4D Gaussian Splatting representation.
+    '''
+
+    # load images in 'assets' folder as examples
+    image_dir = os.path.join(os.path.dirname(__file__), "assets")
+    examples_img = None
+
+    if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
+        examples_4d = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.ply')]
+        examples_img = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.png')]
+    else:
+        examples_4d = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
+        
+    def optimize(image_block: Image.Image):
+        # temporarily only show tiger
+        return f'{os.path.join(os.path.dirname(__file__), "logs")}/tiger.glb', examples_4d
+
+    # Compose demo layout & data flow
+    with gr.Blocks(title=_TITLE, theme=gr.themes.Soft()) as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown('# ' + _TITLE)
+        gr.Markdown(_DESCRIPTION)
+
+        with gr.Row(variant='panel'):
+            left_column = gr.Column(scale=5)
+            with left_column:
+                image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Input image')
+
+                preprocess_chk = gr.Checkbox(True,
+                                             label='Preprocess image automatically (remove background and recenter object)')
+
+            with gr.Column(scale=5):
+                obj3d = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model (Final)")
+                obj4d = Model4DGS(label="4D Model")
+
+            with left_column:
+                gr.Examples(
+                    examples=examples_img,  # NOTE: elements must match inputs list!
+                    inputs=image_block,
+                    outputs=obj3d,
+                    fn=optimize,
+                    label='Examples (click one of the images below to start)',
+                    examples_per_page=40
+                )
+                img_run_btn = gr.Button("Generate 4D")
+
+            # if there is an input image, continue with inference
+            # else display an error message
+            img_run_btn.click(check_img_input, inputs=[image_block], queue=False).success(
+                optimize, inputs=[image_block], outputs=[obj3d, obj4d])
 
 if __name__ == "__main__":
     demo.launch(share=True)

src/README.md

@@ -26,17 +26,73 @@ pip install gradio_model4dgs
 import gradio as gr
 from gradio_model4dgs import Model4DGS
 import os
+from PIL import Image
+import hashlib
 
-image_dir = os.path.join(os.path.dirname(__file__), "assets")
+def check_img_input(control_image):
+    if control_image is None:
+        raise gr.Error("Please select or upload an input image")
 
-if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
-    examples = [os.path.join(image_dir, file) for file in os.listdir(image_dir)]
-else:
-    examples = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        Model4DGS(value=examples, label="4D Model")
+if __name__ == "__main__":
+    _TITLE = '''DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation'''
+
+    _DESCRIPTION = '''
+    <div>
+    <a style="display:inline-block" href="https://jiawei-ren.github.io/projects/dreamgaussian4d/"><img src='https://img.shields.io/badge/public_website-8A2BE2'></a>
+    <a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2312.17142"><img src="https://img.shields.io/badge/2309.16653-f9f7f7?logo=data:image/png;base64,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"></a>
+    <a style="display:inline-block; margin-left: .5em" href='https://github.com/jiawei-ren/dreamgaussian4d'><img src='https://img.shields.io/github/stars/jiawei-ren/dreamgaussian4d?style=social'/></a>
+    </div>
+    We introduce DreamGaussian4D, an efficient 4D generation framework that builds on 4D Gaussian Splatting representation.
+    '''
+
+    # load images in 'assets' folder as examples
+    image_dir = os.path.join(os.path.dirname(__file__), "assets")
+    examples_img = None
+
+    if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
+        examples_4d = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.ply')]
+        examples_img = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.png')]
+    else:
+        examples_4d = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
+        
+    def optimize(image_block: Image.Image):
+        # temporarily only show tiger
+        return f'{os.path.join(os.path.dirname(__file__), "logs")}/tiger.glb', examples_4d
+
+    # Compose demo layout & data flow
+    with gr.Blocks(title=_TITLE, theme=gr.themes.Soft()) as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown('# ' + _TITLE)
+        gr.Markdown(_DESCRIPTION)
+
+        with gr.Row(variant='panel'):
+            left_column = gr.Column(scale=5)
+            with left_column:
+                image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Input image')
+
+                preprocess_chk = gr.Checkbox(True,
+                                             label='Preprocess image automatically (remove background and recenter object)')
+
+            with gr.Column(scale=5):
+                obj3d = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model (Final)")
+                obj4d = Model4DGS(label="4D Model")
+
+            with left_column:
+                gr.Examples(
+                    examples=examples_img,  # NOTE: elements must match inputs list!
+                    inputs=image_block,
+                    outputs=obj3d,
+                    fn=optimize,
+                    label='Examples (click one of the images below to start)',
+                    examples_per_page=40
+                )
+                img_run_btn = gr.Button("Generate 4D")
+
+            # if there is an input image, continue with inference
+            # else display an error message
+            img_run_btn.click(check_img_input, inputs=[image_block], queue=False).success(
+                optimize, inputs=[image_block], outputs=[obj3d, obj4d])
 
 if __name__ == "__main__":
     demo.launch(share=True)

src/demo/app.py

@@ -1,17 +1,73 @@
 import gradio as gr
 from gradio_model4dgs import Model4DGS
 import os
+from PIL import Image
+import hashlib
 
-image_dir = os.path.join(os.path.dirname(__file__), "assets")
+def check_img_input(control_image):
+    if control_image is None:
+        raise gr.Error("Please select or upload an input image")
 
-if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
-    examples = [os.path.join(image_dir, file) for file in os.listdir(image_dir)]
-else:
-    examples = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
+if __name__ == "__main__":
+    _TITLE = '''DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation'''
+
+    _DESCRIPTION = '''
+    <div>
+    <a style="display:inline-block" href="https://jiawei-ren.github.io/projects/dreamgaussian4d/"><img src='https://img.shields.io/badge/public_website-8A2BE2'></a>
+    <a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2312.17142"><img src="https://img.shields.io/badge/2309.16653-f9f7f7?logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADcAAABMCAYAAADJPi9EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAuIwAALiMBeKU/dgAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoAAAa2SURBVHja3Zt7bBRFGMAXUCDGF4rY7m7bAwuhlggKStFgLBgFEkCIIRJEEoOBYHwRFYKilUgEReVNJEGCJJpehHI3M9vZvd3bUP1DjNhEIRQQsQgSHiJgQZ5dv7krWEvvdmZ7d7vHJN+ft/f99pv5XvOtJMFCqvoCUpTdIEeRLC+L9Ox5i3Q9LACaCeK0kXoSChVcD3C/tQPHpAEsquQ73IkUcEz2kcLCknyGW5MGjkljRFVL8xJOKyi4CwCOuQAeAkfTP1+tNxLkogvgEbDgffkJqKqvuMA5ifOpqg/5qWecRstNg7xoUTI1Fovdxg8oy2s5AP8CGeYHmGngeZaOL4I4LXLcpHg4149/GDz4xqgsb+UAbMKKUpkrqHA43MUyyJpWUK0EHeG2YKRXr7tB+QMcgGewLD+ebTDbtrtbBt7UPlhS4rV4IvcDI7J8P1OeA/AcAI7LHljN7aB8XTowJmZt9EFRD/o0SDMH4HlwMhMyDWZZSAHFf3YDs3RS49WDLuaAY3IJq+qzmQKLxXAZKN7oDoYbdV3v5elPqiSpMyiOuAEVZVqHXb1OhloUH+MA+ztO0cAO/RkrfyBE7OAEbAZvO8vzVtTRWFD6DAfY5biBM3PWiaL0a4lvXICwnV8WjmE6ntYmhqX2jjp5LbMZjCw/wbYeN6CizOa2GMVzQOlmHjB4Ceuyk6LJ8huccEmR5Xddg7OOV/NAtchW+E3XbOag60QA4Qwuarca0bRuEJyr+cFQwzcY98huxhAKdQelt4kAQpj4qJ3gvFXAYn+aJumXk1yPlpQUgtIHhbYoFMUstNRRWgjnpl4A7IKlayNymqFHFaWCpV9CFry3LGxR1CgA5kB5M8OX2goApwpaz6mdOMGxtAgXWJySxb4WuQD4qTDgU+N5AAnzpr7ChSWpCyisiQJqY0Y7FtmSKpbV23b45kC0KHBxcQ9QeI8w4KgnHRPVtIU7rOtbioLVg5Hl/qDwSVFAMqLSMSObroCdZYlzIJtMRFVHCaRo/wFWPgaAXzdbBpkc2A4aKzCNd97+URQuESYGDDhIVfWOQIKZJu4D2+oXlgDTV1865gUQZDts756BArMNMoR1oa46BYqbyPixZz1ZUFV3sgwoGBajuBKATl3btIn8QYYMuezRgrsiRUWyr2BxA40EkPMpA/Hm6gbUu7fjEXA3azP6AsbKD9bxdUuhjM9W7fII52BF+daRpE4+WA3P501+jbfmHvQKyFqMuXf7Ot4mkN2fr50y+bRH61X7AXdUpHSxaPQ4GVbR5AGw3g+434XgQGKfr72I+vQRhfsu92dOx7WicInzt3CBg1RVpMm0NveWo2SqFzgmdNZMbriILD+S+zoueWf2vSdAipzacWN5nMl6XxNlUHa/J8DoJodUDE0HR8Ll5V0lPxcrLEHZPV4AzS83OLis7FowVa3RSku7BSNxJqQAlN3hBTC2apmDSkpaw22wJemGQFUG7J4MlP3JC6A+f96V7vRyX9It3nzT/GrjIU8edM7rMSnIi10f476lzbE1K7yEiEuWro0OJBguLCwDuFOJc1Na6sRWL/cCeMIwUN9ggSVbe3v/5/EgzTKWLvEAiBrYRUkgwNI2ZaFQNT75UDxEUEx97zYnzpmiLEmbaYCbNxYtFAb0/Z4AztgUrhyxuNgxPnhfHFDHz/vTgFWUQZxTRkkJhQ6YNdVUEPAfO6ZV5BRss6LcCVb7VaAma9giy0XJZBt9IQh42NY0NSdgbLIPlLUF6rEdrdt0CUCK1wsCbkcI3ZSLc7ZSwGLbmJXbPsNxnE5xilYKAobZ77LpGZ8TAIun+/iCKQoF71IxQDI3K2CCd+ARNvXg9sykBcnHAoCZG4u66hlDoQLe6QV4CRtFSxZQ+D0BwNO2jgdkzoGoah1nj3FVlSR19taTSYxI8QLut23U8dsgzqHulJNCQpcqBnpTALCuQ6NSYLHpmR5i42gZzuIdcrMMvMJbQlxe3jXxyZnLACl7ARm/FjPIDOY8ODtpM71sxwfcZpvBeUzKWmfNINM5AS+wO0Khh7dMqKccu4+qatarZjYAwDlgetzStHtEt+XedsBOQtU9XMrRgjg4KTnc5nr+dmqadit/4C4uLm8DuA9koJTj1TL7fI5nDL+qqoo/FLGAzL7dYT17PzvAcQONYSUQRxW/QMrHZVIyik0ZuQA2mzp+Ji8BW4YM3Mbzm9inaHkJCGfrUZZjujiYailfFwA8DHIy3acwUj4v9vUVa+SmgNsl5fuyDTKovW9/IAmfLV0Pi2UncA515kjYdrwC9i9rpuHiq3JwtAAAAABJRU5ErkJggg=="></a>
+    <a style="display:inline-block; margin-left: .5em" href='https://github.com/jiawei-ren/dreamgaussian4d'><img src='https://img.shields.io/github/stars/jiawei-ren/dreamgaussian4d?style=social'/></a>
+    </div>
+    We introduce DreamGaussian4D, an efficient 4D generation framework that builds on 4D Gaussian Splatting representation.
+    '''
+
+    # load images in 'assets' folder as examples
+    image_dir = os.path.join(os.path.dirname(__file__), "assets")
+    examples_img = None
+
+    if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
+        examples_4d = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.ply')]
+        examples_img = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.png')]
+    else:
+        examples_4d = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
+        
+    def optimize(image_block: Image.Image):
+        # temporarily only show tiger
+        return f'{os.path.join(os.path.dirname(__file__), "logs")}/tiger.glb', examples_4d
+
+    # Compose demo layout & data flow
+    with gr.Blocks(title=_TITLE, theme=gr.themes.Soft()) as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown('# ' + _TITLE)
+        gr.Markdown(_DESCRIPTION)
+
+        with gr.Row(variant='panel'):
+            left_column = gr.Column(scale=5)
+            with left_column:
+                image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Input image')
+
+                preprocess_chk = gr.Checkbox(True,
+                                             label='Preprocess image automatically (remove background and recenter object)')
+
+            with gr.Column(scale=5):
+                obj3d = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model (Final)")
+                obj4d = Model4DGS(label="4D Model")
+
+            with left_column:
+                gr.Examples(
+                    examples=examples_img,  # NOTE: elements must match inputs list!
+                    inputs=image_block,
+                    outputs=obj3d,
+                    fn=optimize,
+                    label='Examples (click one of the images below to start)',
+                    examples_per_page=40
+                )
+                img_run_btn = gr.Button("Generate 4D")
 
-with gr.Blocks() as demo:
-    with gr.Row():
-        Model4DGS(value=examples, label="4D Model")
+            # if there is an input image, continue with inference
+            # else display an error message
+            img_run_btn.click(check_img_input, inputs=[image_block], queue=False).success(
+                optimize, inputs=[image_block], outputs=[obj3d, obj4d])
 
 if __name__ == "__main__":
     demo.launch(share=True)

src/demo/logs/0d6e37451ce97b5e97dd74e63f9a06b17644f2ad02f32534ce079493808d288f.glb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36c0b461d6973a61b97cfdcffb49c1e6e2ac2730cda3da2a53ca18eb1300c21d
+size 2101240

src/demo/logs/_tmp_gradio_312f2981ccbecf05b94e9729c5e88e720cafa46c_1e5133dcc83642c805644360a403f7c38a9dffc7f860cb96a678c76708d997c0.glb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f898caa81d1c4eebae1bd8a3dd0581fd787290abc7c174051c0ab0366bb231f
+size 2614792

src/demo/logs/_tmp_gradio_6a883f05b5f52ebd382b11a3aef0825b272d0837_2f78ca4365556592bd54881407d40b3928870cfa6d89ec389b65356b3759c96c.glb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f4b8d60a1208343c984b7bb129efb0a175b7f7dddd737e98e0302f4931f6dda
+size 2711984

src/demo/logs/tiger_rgba.glb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f898caa81d1c4eebae1bd8a3dd0581fd787290abc7c174051c0ab0366bb231f
+size 2614792

src/demo/space.py

@@ -41,17 +41,73 @@ pip install gradio_model4dgs
 import gradio as gr
 from gradio_model4dgs import Model4DGS
 import os
+from PIL import Image
+import hashlib
 
-image_dir = os.path.join(os.path.dirname(__file__), "assets")
+def check_img_input(control_image):
+    if control_image is None:
+        raise gr.Error("Please select or upload an input image")
 
-if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
-    examples = [os.path.join(image_dir, file) for file in os.listdir(image_dir)]
-else:
-    examples = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        Model4DGS(value=examples, label="4D Model")
+if __name__ == "__main__":
+    _TITLE = '''DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation'''
+
+    _DESCRIPTION = '''
+    <div>
+    <a style="display:inline-block" href="https://jiawei-ren.github.io/projects/dreamgaussian4d/"><img src='https://img.shields.io/badge/public_website-8A2BE2'></a>
+    <a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2312.17142"><img src="https://img.shields.io/badge/2309.16653-f9f7f7?logo=data:image/png;base64,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"></a>
+    <a style="display:inline-block; margin-left: .5em" href='https://github.com/jiawei-ren/dreamgaussian4d'><img src='https://img.shields.io/github/stars/jiawei-ren/dreamgaussian4d?style=social'/></a>
+    </div>
+    We introduce DreamGaussian4D, an efficient 4D generation framework that builds on 4D Gaussian Splatting representation.
+    '''
+
+    # load images in 'assets' folder as examples
+    image_dir = os.path.join(os.path.dirname(__file__), "assets")
+    examples_img = None
+
+    if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
+        examples_4d = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.ply')]
+        examples_img = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.png')]
+    else:
+        examples_4d = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
+        
+    def optimize(image_block: Image.Image):
+        # temporarily only show tiger
+        return f'{os.path.join(os.path.dirname(__file__), "logs")}/tiger.glb', examples_4d
+
+    # Compose demo layout & data flow
+    with gr.Blocks(title=_TITLE, theme=gr.themes.Soft()) as demo:
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown('# ' + _TITLE)
+        gr.Markdown(_DESCRIPTION)
+
+        with gr.Row(variant='panel'):
+            left_column = gr.Column(scale=5)
+            with left_column:
+                image_block = gr.Image(type='pil', image_mode='RGBA', height=290, label='Input image')
+
+                preprocess_chk = gr.Checkbox(True,
+                                             label='Preprocess image automatically (remove background and recenter object)')
+
+            with gr.Column(scale=5):
+                obj3d = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model (Final)")
+                obj4d = Model4DGS(label="4D Model")
+
+            with left_column:
+                gr.Examples(
+                    examples=examples_img,  # NOTE: elements must match inputs list!
+                    inputs=image_block,
+                    outputs=obj3d,
+                    fn=optimize,
+                    label='Examples (click one of the images below to start)',
+                    examples_per_page=40
+                )
+                img_run_btn = gr.Button("Generate 4D")
+
+            # if there is an input image, continue with inference
+            # else display an error message
+            img_run_btn.click(check_img_input, inputs=[image_block], queue=False).success(
+                optimize, inputs=[image_block], outputs=[obj3d, obj4d])
 
 if __name__ == "__main__":
     demo.launch(share=True)