We introduce Iris3D, a novel 3D content generation system that generates vivid textures and detailed 3D shapes while preserving the input information. Our system integrates a Multi-View Large Reconstruction Model (MVLRM 25) to generate a coarse 3D mesh and introduces a novel optimization scheme called Synchronized Diffusion Distillation (SDD) for refinement. Unlike previous refined methods based on Score Distillation Sampling (SDS), which suffer from unstable optimization and geometric over-smoothing due to ambiguities across different views and modalities, our method effectively distills consistent multi-view and multi-modal priors from 2D diffusion models in a training-free manner. This enables robust optimization of 3D representations. Additionally, because SDD is training-free, it preserves the diffusion’s prior knowledge and mitigates potential degradation. This characteristic makes it highly compatible with advanced 2D diffusion techniques like IP-Adapters and ControlNet, allowing for more controllable 3D generation with additional conditioning signals. Experiments demonstrate that our method produces high-quality 3D results with plausible textures and intricate geometric details.
Liang et al. (Thu,) studied this question.