IT3D: Improved Text-to-3D Generation with Explicit View Synthesis
Yiwen Chen Chi Zhang Xiaofeng Yang Zhongang Cai Gang Yu Lei Yang Guosheng Lin
Recent strides in Text-to-3D techniques have been propelled by distilling
knowledge from powerful large text-to-image diffusion models (LDMs).
Nonetheless, existing Text-to-3D approaches often grapple with challenges such
as over-saturation, inadequate detailing, and unrealistic outputs. This study
presents a novel strategy that leverages explicitly synthesized multi-view
images to address these issues. Our approach involves the utilization of
image-to-image pipelines, empowered by LDMs, to generate posed high-quality
images based on the renderings of coarse 3D models. Although the generated
images mostly alleviate the aforementioned issues, challenges such as view
inconsistency and significant content variance persist due to the inherent
generative nature of large diffusion models, posing extensive difficulties in
leveraging these images effectively. To overcome this hurdle, we advocate
integrating a discriminator alongside a novel Diffusion-GAN dual training
strategy to guide the training of 3D models. For the incorporated
discriminator, the synthesized multi-view images are considered real data,
while the renderings of the optimized 3D models function as fake data. We
conduct a comprehensive set of experiments that demonstrate the effectiveness
of our method over baseline approaches.