Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior
This addresses the issue of multi-view inconsistency in 3D generation for applications like computer graphics and virtual reality, representing an incremental improvement over existing methods.
The paper tackles the problem of inconsistent appearances and inaccurate shapes in text-to-3D generation by introducing Sculpt3D, a framework that injects 3D priors from reference objects without retraining the 2D diffusion model, resulting in improved multi-view consistency while retaining fidelity and diversity.
Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g., faces on the back view) and inaccurate shapes (e.g., animals with extra legs). Existing methods mainly address this issue by retraining diffusion models with images rendered from 3D data to ensure multi-view consistency while struggling to balance 2D generation quality with 3D consistency. In this paper, we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically, we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. Moreover, to ensure accurate appearances of different views, we further modulate the output of the 2D diffusion model to the correct patterns of the template views without altering the generated object's style. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model. Extensive experiments show our method can largely improve the multi-view consistency while retaining fidelity and diversity. Our project page is available at: https://stellarcheng.github.io/Sculpt3D/.