Compare CSM AI vs. GET3D

Produce assets featuring detailed geometry, UV-unwrapped textures, and neural radiance fields, leveraging cutting-edge advancements in neural inverse graphics. The process of designing environments and games has become quicker and more precise than it has been in the past. Develop captivating 3D simulators and games on an exceptional scale. Craft personalized textured 3D assets with ease. Generations are facilitated on high-performance and specialized servers. The 3D outputs remain confidential, with dedicated support offered, as well as tailored training and data solutions to meet individual needs. This new approach empowers creators to push the boundaries of interactive experiences.

We create a three-dimensional signed distance field (SDF) and a textured field using two latent codes. DMTet is employed to derive a 3D surface mesh from the SDF, and we sample the texture field at the surface points to obtain color information. Our training incorporates adversarial losses focused on 2D images, specifically utilizing a rasterization-based differentiable renderer to produce both RGB images and silhouettes. To distinguish between genuine and generated inputs, we implement two separate 2D discriminators—one for RGB images and another for silhouettes. The entire framework is designed to be trainable in an end-to-end manner. As various sectors increasingly transition towards the development of expansive 3D virtual environments, the demand for scalable tools that can generate substantial quantities of high-quality and diverse 3D content has become apparent. Our research endeavors to create effective 3D generative models capable of producing textured meshes that can be seamlessly integrated into 3D rendering engines, thereby facilitating their immediate application in various downstream uses. This approach not only addresses the scalability challenge but also enhances the potential for innovative applications in virtual reality and gaming.