  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.
