An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion
Xingguang (Kevin) Yan ⋅ Han-Hung Lee ⋅ Ziyu Wan ⋅ Angel Chang
2024 Poster
in
Workshop: Generative AI and Creativity: A dialogue between machine learning researchers and creative professionals
in
Workshop: Generative AI and Creativity: A dialogue between machine learning researchers and creative professionals
Abstract
This paper presents "Object Images," a novel method that represents 3D shapes with UV-patch structures as 64x64 pixel images, encapsulating geometry and appearance details. By converting complex 3D shapes into a 2D format, we leverage image generation models like Diffusion Transformers to produce realistic 3D models with UV-patches. This patch-based format naturally describes local regions of the generated object, making editing easier and enhancing the creative design process.
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