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Phidias A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion discuss: In 3D modeling, designers often use an existing 3D model as a reference to create new ones. This practice has inspired the development of Phidias, a novel generative model that uses... diffusion for reference-augmented 3D generation. Given an image, our method leverages a retrieved or user-provided 3D reference model to guide the generation process, thereby enhancing the generation quality, generalization ability, and controllability. Our model integrates three key components: 1) meta-ControlNet that dynamically modulates the conditioning strength, 2) dynamic reference routing that mitigates misalignment between the input image and 3D reference, and 3) self-reference augmentations that enable self-supervised training with a progressive curriculum. Collectively, these designs result in a clear improvement over existing methods. Phidias establishes a unified framework for 3D generation using text, image, and 3D conditions with versatile applications.show more

AK

403,508 subscribers

25,120 次观看 • 1 年前 •via X (Twitter)

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Tengfei Wang 的头像

Tengfei Wang1 年前

Glad to share 🔥Project Phidias What sparks will fly when combining ✨RAG✨ (Retrieval-Augmented Generation) and ✨3D Diffusion✨? Phidias supports 3D Gen from texts, images, and 3D models, with improved quality! Project: - Paper:

Bohan Zeng 的头像

Bohan Zeng1 年前

Excellent work! I’d like to share a method I previously developed for aligning a given image prompt with a provided basic 3D model:

Stella X 的头像

Stella X1 年前

👀

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