Direct and Explicit 3D Generation from a Single Image
Haoyu Wu, Meher Gitika Karumuri, Chuhang Zou, Seungbae Bang, Yuelong, Li, Dimitris Samaras, Sunil Hadap

TL;DR
This paper presents a fast, scalable method for generating detailed 3D models from a single image by combining multi-view 2D data with a modified diffusion model, improving quality and efficiency.
Contribution
The authors introduce a novel framework that directly produces explicit 3D surface geometry and textures from a single image using multi-view data and a modified Stable Diffusion model.
Findings
Outperforms existing methods in geometry and texture quality.
Achieves faster 3D generation times.
Produces high-resolution, detailed 3D models from a single image.
Abstract
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…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · Advanced Vision and Imaging
Methods*Communicated@Fast*How Do I Communicate to Expedia? · Softmax · Attention Is All You Need · Concatenated Skip Connection · Convolution · Max Pooling · Diffusion · U-Net
