HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a Single Image

Tong Wu, Zhibing LI, Shuai Yang, Pan Zhang, Xinggang Pan, Jiaqi Wang, Dahua Lin, Ziwei Liu

HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a Single Image: 22 upvotes on Hugging Face Daily Papers, #3 of 12 papers on 2023-12-08. Day-by-day upvote history.

3D content creation from a single image is a long-standing yet highly desirable task. Recent advances introduce 2D diffusion priors, yielding reasonable results. However, existing methods are not hyper-realistic enough for post-generation usage, as users cannot view, render and edit the resulting 3D content from a full range. To address these challenges, we introduce HyperDreamer with several key designs and appealing properties: 1) Viewable: 360 degree mesh modeling with high-resolution textures enables the creation of visually compelling 3D models from a full range of observation points. 2) Renderable: Fine-grained semantic segmentation and data-driven priors are incorporated as guidance to learn reasonable albedo, roughness, and specular properties of the materials, enabling semantic-aware arbitrary material estimation. 3) Editable: For a generated model or their own data, users can interactively select any region via a few clicks and efficiently edit the texture with text-based guidance. Extensive experiments demonstrate the effectiveness of HyperDreamer in modeling region-aware materials with high-resolution textures and enabling user-friendly editing. We believe that HyperDreamer holds promise for advancing 3D content creation and finding applications in various domains.

Paper page on Hugging Face · arXiv

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