Update README.md

README.md

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 Arxiv: https://arxiv.org/abs/2404.07191
 
-We present InstantMesh, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability. By synergizing the strengths of an off-the-shelf multiview diffusion model and a sparse-view reconstruction model based on the LRM architecture, InstantMesh is able to create diverse 3D assets within 10 seconds. To enhance the training efficiency and exploit more geometric supervisions, e.g., depths and normals, we integrate a differentiable iso-surface extraction module into our framework and directly optimize on the mesh representation. Experimental results on public datasets demonstrate that InstantMesh significantly outperforms other latest image-to-3D baselines, both qualitatively and quantitatively. We release all the code, weights, and demo of InstantMesh, with the intention that it can make substantial contributions to the community of 3D generative AI and empower both researchers and content creators.
+🌟 Presenting InstantMesh: Revolutionizing Image-to-3D Transformation with Unmatched Speed and Quality 🖼️➡️🎨
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+InstantMesh: pioneering framework designed to seamlessly convert single images into intricate 3D models in a matter of seconds. Built upon a foundation of advanced technology and meticulous research, InstantMesh stands at the forefront of rapid 3D mesh generation. 🚀
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+Through a harmonious fusion of off-the-shelf multiview diffusion models and sparse-view reconstruction architectures based on the LRM paradigm, InstantMesh achieves unparalleled generation quality while maintaining remarkable scalability. This synergy enables the creation of diverse 3D assets with unprecedented efficiency, all within a remarkably short timeframe of 10 seconds. 💡
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+In our relentless pursuit of excellence, we have integrated a differentiable iso-surface extraction module into InstantMesh, enhancing training efficiency and enabling the utilization of additional geometric supervisions such as depths and normals. This innovation further elevates the fidelity and versatility of the generated 3D assets. 🔄💎
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+Empirical evaluations conducted on public datasets demonstrate the superior performance of InstantMesh when compared to contemporary image-to-3D baselines, both in terms of qualitative output and quantitative metrics. 📊🏆
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+As a testament to our commitment to fostering collaboration and innovation within the community, we are pleased to announce the release of all associated code, weights, and a comprehensive demo of InstantMesh. It is our fervent hope that this contribution will catalyze advancements in the realm of 3D generative AI, empowering researchers and content creators alike to push the boundaries of creativity and possibility. 🌐💡
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+Experience the future of image-to-3D transformation with InstantMesh – where speed, quality, and innovation converge to unlock limitless creative potential. 🌟🖼️🚀