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  1. app.py +2 -2
app.py CHANGED
@@ -267,7 +267,7 @@ with tempfile.TemporaryDirectory() as tmpdir:
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  [![arXiv](https://img.shields.io/badge/arXiv-2405.17251-red?logo=arxiv)](https://arxiv.org/abs/2405.17251)
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  ## Introduction
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- This is an official demo for the paper "[GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping](https://genwarp-nvs.github.io/)". Genwarp can generate novel view images from a single input conditioned on camera poses. In this demo, we offer a basic use of inference of the model. For detailed information, please refer the [paper](https://arxiv.org/abs/2405.17251).
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  ## How to Use
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  1. Upload a reference image to "Reference Input"
@@ -276,7 +276,7 @@ with tempfile.TemporaryDirectory() as tmpdir:
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  3. Hit "Generate a novel view" button and check the result
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  ## Tips
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- - Extremely large camera movement from the input view might cause low performance results due to the unexpected deviation from the training distribution, which is not the scope this model. Instead, you can feed the generation result for the small camera movement repeatedly and progressively move towards the desired view.
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  - 3D viewer might take some time to update especially when trying different images back to back. Wait until it fully updates to the new image.
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  """
 
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  [![arXiv](https://img.shields.io/badge/arXiv-2405.17251-red?logo=arxiv)](https://arxiv.org/abs/2405.17251)
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  ## Introduction
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+ This is an official demo for the paper "[GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping](https://genwarp-nvs.github.io/)". Genwarp can generate novel view images from a single input conditioned on camera poses. In this demo, we offer a basic use of inference of the model. For detailed information, please refer to the [paper](https://arxiv.org/abs/2405.17251).
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  ## How to Use
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  1. Upload a reference image to "Reference Input"
 
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  3. Hit "Generate a novel view" button and check the result
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  ## Tips
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+ - Extremely large camera movement from the input view might cause low performance results due to the unexpected deviation from the training distribution, which is not the scope of this model. Instead, you can feed the generation result for the small camera movement repeatedly and progressively move towards the desired view.
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  - 3D viewer might take some time to update especially when trying different images back to back. Wait until it fully updates to the new image.
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  """