Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion

Yuanxun Lu1, Jingyang Zhang2, Shiwei Li2, Tian Fang2, David McKinnon2, Yanghai Tsin2, Long Quan3, Xun Cao1, Yao Yao1
1Nanjing University, 2Apple, 3The Hong Kong University of Science and Technology
Paper arXiv

TL;DR:   Diverse, Janus-free, and high-fidelity 3D content generation in only 10 seconds.

Results Gallery. Given text prompts as description input, our method outputs high-quality textured triangle mesh in only 10 seconds. The generated multi-view normal and RGB images are shown beside the rendered models.

Prompts for the above left column results are R1) a baby bunny sitting on top of a stack of pancakes, R2) a beautiful rainbow fish, R3) a DSLR photo of an astronaut standing on the surface of mars, R4) a steam engine train, high resolution, R5) a DSLR photo of a delicious croissant, and R6) a beautiful dress made out of garbage bags, on a mannequin. Studio lighting, high quality, high resolution. Prompts for the above right column results are R1) a bald eagle carved out of wood, R2) a DSLR photo of a robot tiger, R3) a DSLR photo of a teal moped, R4) a turtle standing on its hind legs, wearing a top hat and holding a cane, R5) a zoomed out DSLR photo of a marble bust of a fox head, and R6) a DSLR photo of a corgi puppy.

Abstract

Recent advances in generative AI have unveiled significant potential for the creation of 3D content. However, current methods either apply a pre-trained 2D diffusion model with the time-consuming score distillation sampling (SDS), or a direct 3D diffusion model trained on limited 3D data losing generation diversity. In this work, we approach the problem by employing a multi-view 2.5D diffusion fine-tuned from a pre-trained 2D diffusion model. The multi-view 2.5D diffusion directly models the structural distribution of 3D data, while still maintaining the strong generalization ability of the original 2D diffusion model, filling the gap between 2D diffusion-based and direct 3D diffusion-based methods for 3D content generation. During inference, multi-view normal maps are generated using the 2.5D diffusion, and a novel differentiable rasterization scheme is introduced to fuse the almost consistent multi-view normal maps into a consistent 3D model. We further design a normal-conditioned multi-view image generation module for fast appearance generation given the 3D geometry. Our method is a one-pass diffusion process and does not require any SDS optimization as post-processing. We demonstrate through extensive experiments that, our direct 2.5D generation with the specially-designed fusion scheme can achieve diverse, mode-seeking-free, and high-fidelity 3D content generation in only 10 seconds.

Pipeline

Interpolate start reference image.

Overview of our text-to-3D content generation system. The generation is a two-stage process, first generating geoemtry and then appearance. Specifically, the system is composed of the following steps: 1) a single denoising process to simultaneously generate 4 normal maps; 2) fast mesh optimization by differentiable rasterization; 3) a single denoising process to generate 4 images conditioned on rendered normal maps; 4) texture construction from multi-view images. The whole generation process only takes 10 seconds.

Fast and Explicit Multi-view 2.5D Fusion

Our method fuses the multi-view normal images into an explicit triangle mesh via a fast meshing algorithm. The geometry optimization process via differentiable rasterization only requires only 2-3 seconds to generate a high-quality result.

Diverse Generation

Diverse Generation. Our method avoids the common mode-seeking problem by SDS and generates diverse results. Prompts for the results from top to bottom are: R1) a ceramic lion, R2) a DSLR photo of a human skull, R3) a DSLR photo of a corgi puppy, R4) a DSLR photo of a pirate collie dog, high resolution, R5) a DSLR photo of a toy robot, R6) a blue motorcycle, R7) a DSLR photo of an ice cream sundae, and R8) a zoomed out DSLR photo of a wizard raccoon casting a spell.

Geometry-Appearance Disentangled Generation

Geometry-Appearance Disentangled Generation. Prompts for the results from top to bottom are: R1) a DSLR photo of a bear dressed in medieval armor, R2) a zoomed out DSLR photo of a wizard raccoon casting a spell, R3) a DSLR photo of a robot tiger, and R4) a freshly baked loaf of sourdough bread on a cutting board.

Comparison to Previous Text-to-3D Methods

Prompts for the results from top to bottom are: R1) a zoomed out DSLR photo of a wizard raccoon casting a spell, R2) a DSLR photo of a turtle standing on its hind legs, wearing a top hat and holding a cane, R3) a DSLR photo of a pirate collie dog, high resolution, and R4) a DSLR photo of a robot tiger.

More Results

More generation results. The prompts for the results above can be found at here.