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.

翻译：从单张图像创建三维内容是一项长期且极具价值的研究目标。近期研究引入二维扩散先验方法取得了合理成果，但现有方法在生成后的实际应用中仍缺乏足够的超逼真度，用户无法对生成的三维内容进行全方位观察、渲染与编辑。针对这些挑战，我们提出HyperDreamer，其关键设计与特性包括：1）可观察性：通过高分辨率纹理的360度网格建模，实现从全视角观测点创建视觉震撼的三维模型。2）可渲染性：融合细粒度语义分割与数据驱动先验作为引导，学习材料合理的反照率、粗糙度与镜面属性，实现语义感知的任意材质估计。3）可编辑性：对于生成模型或用户自有数据，用户可通过数次点击交互式选择任意区域，并基于文本引导高效编辑纹理。大量实验证明HyperDreamer在区域感知材质建模、高分辨率纹理生成及用户友好编辑方面的有效性。我们相信HyperDreamer将推动三维内容创作技术发展，并在多个领域找到应用场景。