Virtuall vs. GET3D

Virtuall stands out as an advanced, cloud-driven 3D collaboration environment tailored for modern studios, creators, and teams enhanced by AI, seamlessly combining human ingenuity with generative AI processes all within one cohesive platform. It offers extensive asset and project management functionalities, including task distribution, oversight of production, cloud-based storage, version control, tagging systems, revision tracking, and auditing capabilities. Artists are empowered to swiftly generate 3D models from a variety of sources such as sketches, images, text, or multifaceted inputs, assess results from different AI engines, and export models in either high or low polygon formats for various uses. Users can conveniently render, preview, comment on, share, and analyze models directly through their browsers, benefiting from robust version control, integration with platforms like Slack and Jira, immediate sharing options, and compatibility with over 400 format combinations for comprehensive 3D conversion. The platform is equipped with enhanced collaboration tools, providing features like real-time feedback, organized comment management, stakeholder review capabilities, and insights into production metrics, ensuring that workflows remain transparent and efficient. This groundbreaking workspace not only boosts creative processes but also cultivates effective teamwork among users, ultimately leading to more polished and successful project results. Furthermore, the combination of these features allows teams to iterate quickly, making adjustments based on direct feedback, which is crucial in today’s fast-paced creative industries.

We develop a three-dimensional signed distance field (SDF) alongside a textured field using two latent codes. To extract a 3D surface mesh from the SDF, we utilize DMTet, sampling the texture field at surface points for color information. Our training process includes adversarial losses centered on 2D images, employing a rasterization-based differentiable renderer to generate both RGB visuals and silhouettes. To differentiate between real and generated inputs, we introduce two distinct 2D discriminators—one dedicated to RGB images and the other to silhouettes. The entire system is structured to enable end-to-end training. As various industries shift towards creating expansive 3D virtual environments, the necessity for scalable tools capable of generating large volumes of high-quality and diverse 3D content becomes increasingly evident. Our research aims to develop robust 3D generative models that produce textured meshes, facilitating their seamless integration into 3D rendering engines for immediate deployment in a range of applications. This strategy not only addresses the challenge of scalability but also opens up new avenues for innovative uses in fields like virtual reality and gaming. Moreover, by enhancing the quality and diversity of 3D content, we aim to push the boundaries of creativity and interactivity within these immersive environments.