# Image to 3D

Hunyuan3D 2.0 is an advanced large-scale 3D synthesis system developed by Tencent, focusing on generating high-resolution textured 3D assets. The system comprises two core components: the large-scale shape generation model Hunyuan3D-DiT and the large-scale texture synthesis model Hunyuan3D-Paint. By decoupling the challenges of shape and texture generation, it provides users with a flexible platform for 3D asset creation. The system surpasses existing open-source and closed-source models in geometric details, conditional alignment, texture quality, and more, showcasing high practicality and innovation. Currently, the inference code and pre-trained models of this model are open-sourced, allowing users to quickly experience it through the official website or Hugging Face space.

This AI system can create a 3D world from a single image, allowing users to immerse themselves in any picture for 3D exploration. This technology improves control and consistency, transforming the way we create films, games, simulators, and other digital expressions. It represents a significant step in spatial intelligence, enabling users to experience various camera effects and 3D effects while exploring classic artworks in real-time within their browser.

Stable Fast 3D (SF3D) is a large reconstruction model based on TripoSR that can create textured UV-mapped 3D mesh assets from a single object image. The model is highly trained and can produce a 3D model in less than a second, offering a low polygon count along with UV mapping and texture processing, making it easier to use the model in downstream applications such as game engines or rendering tasks. Additionally, the model predicts material parameters (roughness, metallic) for each object, enhancing reflective behaviors during rendering. SF3D is ideal for fields that require rapid 3D modeling, such as game development and visual effects production.

Ouroboros3D is a unified 3D generation framework that integrates multi-view image generation and 3D reconstruction into a single recursive diffusion process. The framework jointly trains the two modules via a self-supervised mechanism, enabling them to adapt to each other and achieve robust inference. During multi-view denoising, the multi-view diffusion model utilizes 3D-aware rendered images from the reconstruction module at the previous timestep as additional conditioning. The combination of the recursive diffusion framework with 3D-aware feedback improves the overall geometric consistency of the process. Experiments demonstrate that the Ouroboros3D framework outperforms both separate training of the two stages and existing methods that combine them at inference time.

Repaint123 can generate high-quality, multi-view consistent 3D content in just 2 minutes from a single image. It combines the robust image generation capability of the 2D scattering model with the texture alignment ability of the progressive redraw strategy, creating high-quality, visually consistent multi-view images. It enhances the image quality during the redraw process by adjusting the redraw intensity based on visibility perception, making the generation of high-quality, multi-view consistent images achievable with a simple mean squared error loss function. The resulting high-quality, multi-view consistent images enable the rapid generation of 3D content.