Direct and Explicit 3D Generation from a Single Image
This work addresses the challenge of efficient and high-quality 3D generation from images for applications in computer graphics and vision, representing a novel method for a known bottleneck.
The paper tackles the problem of high computational costs and lack of scalability in image-to-3D generation by introducing a framework that directly generates explicit surface geometry and texture from a single image, achieving significantly faster generation time and surpassing existing baselines in quality.
Current image-to-3D approaches suffer from high computational costs and lack scalability for high-resolution outputs. In contrast, we introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D depth and RGB images along with 3D Gaussian features using a repurposed Stable Diffusion model. We introduce a depth branch into U-Net for efficient and high quality multi-view, cross-domain generation and incorporate epipolar attention into the latent-to-pixel decoder for pixel-level multi-view consistency. By back-projecting the generated depth pixels into 3D space, we create a structured 3D representation that can be either rendered via Gaussian splatting or extracted to high-quality meshes, thereby leveraging additional novel view synthesis loss to further improve our performance. Extensive experiments demonstrate that our method surpasses existing baselines in geometry and texture quality while achieving significantly faster generation time.