Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation
Authors: name: Minglin Chen, name: Longguang Wang, name: Weihao Yuan, name: Yukun Wang, name: Zhe Sheng, name: Yisheng He, name: Zilong Dong, name: Liefeng Bo, name: Yulan Guo

Recently, text-to-3D approaches have achieved high-fidelity 3D content
generation using text description. However, the generated objects are
stochastic and lack fine-grained control. Sketches provide a cheap approach to
introduce such fine-grained control. Nevertheless, it is challenging to achieve
flexible control from these sketches due to their abstraction and ambiguity. In
this paper, we present a multi-view sketch-guided text-to-3D generation
framework (namely, Sketch2NeRF) to add sketch control to 3D generation.
Specifically, our method leverages pretrained 2D diffusion models (e.g., Stable
Diffusion and ControlNet) to supervise the optimization of a 3D scene
represented by a neural radiance field (NeRF). We propose a novel synchronized
generation and reconstruction method to effectively optimize the NeRF. In the
experiments, we collected two kinds of multi-view sketch datasets to evaluate
the proposed method. We demonstrate that our method can synthesize 3D
consistent contents with fine-grained sketch control while being high-fidelity
to text prompts. Extensive results show that our method achieves
state-of-the-art performance in terms of sketch similarity and text alignment.