{"id":2134,"date":"2023-08-25T03:17:16","date_gmt":"2023-08-25T03:17:16","guid":{"rendered":"https:\/\/mlnews.dev\/?p=2134"},"modified":"2023-12-03T14:43:14","modified_gmt":"2023-12-03T14:43:14","slug":"it3d-revolution-enhanced-text-to-3d-generation","status":"publish","type":"post","link":"https:\/\/mlnews.dev\/it3d-revolution-enhanced-text-to-3d-generation\/","title":{"rendered":"IT3D Revolution: Enhanced Text-to-3D Generation with Empowered View Synthesis"},"content":{"rendered":"\n
Get ready to be amazed by IT3D’s Incredible Text-to-3D Enhancement! Experience the excitement of 3D models like never before! Yiwen Chen<\/strong><\/em> and Chi Zhang<\/strong><\/em>, along with the collaborative efforts of researchers from S-Lab <\/strong><\/em>and Nanyang Technological University<\/em><\/strong>, this pioneering text-to-3D enhancement benefits from their contributions, pushing the boundaries of 3D generation technology and its potential applications.<\/p>\n\n\n\n This research offers a solution to the challenges faced in text-to-3D generation, including issues with over-saturation, lack of detail, and unrealistic outputs. By employing an innovative strategy of synthesizing multi-view images and utilizing image-to-image pipelines, the approach enhances the quality of generated 3D models. Additionally, the integration of a discriminator and a Diffusion-GAN training strategy improves stability and quality.<\/p>\n\n\n