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📢 Multi-View Image to 3D just got a major upgrade! Now your textures generate from multiple views too. So a few references of the same object bring the whole model back with much higher accuracy and consistency, from structure to texture.

MeshyAI

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130,481 次观看 • 6 天前 •via X (Twitter)

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🔥 New Feature: Multi-Image to 3D! Now you can upload multiple images of the same object to generate a more complete and accurate 3D model. 😉 More improvements coming soon! Try it out and let us know what you think. #3dmodeling #ai

🔥 New Feature: Multi-Image to 3D! Now you can upload multiple images of the same object to generate a more complete and accurate 3D model. 😉 More improvements coming soon! Try it out and let us know what you think. #3dmodeling #ai

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MVDream: Multi-view Diffusion for 3D Generation paper page: propose MVDream, a multi-view diffusion model that is able to generate geometrically consistent multi-view images from a given text prompt. By leveraging image diffusion models pre-trained on large-scale web datasets and a multi-view dataset rendered from 3D assets, the resulting multi-view diffusion model can achieve both the generalizability of 2D diffusion and the consistency of 3D data. Such a model can thus be applied as a multi-view prior for 3D generation via Score Distillation Sampling, where it greatly improves the stability of existing 2D-lifting methods by solving the 3D consistency problem. Finally, we show that the multi-view diffusion model can also be fine-tuned under a few shot setting for personalized 3D generation, i.e. DreamBooth3D application, where the consistency can be maintained after learning the subject identity.

MVDream: Multi-view Diffusion for 3D Generation paper page: propose MVDream, a multi-view diffusion model that is able to generate geometrically consistent multi-view images from a given text prompt. By leveraging image diffusion models pre-trained on large-scale web datasets and a multi-view dataset rendered from 3D assets, the resulting multi-view diffusion model can achieve both the generalizability of 2D diffusion and the consistency of 3D data. Such a model can thus be applied as a multi-view prior for 3D generation via Score Distillation Sampling, where it greatly improves the stability of existing 2D-lifting methods by solving the 3D consistency problem. Finally, we show that the multi-view diffusion model can also be fine-tuned under a few shot setting for personalized 3D generation, i.e. DreamBooth3D application, where the consistency can be maintained after learning the subject identity.

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