Phidias - A retrieval-enhanced 3D content generation model that supports multimodal input.

What is Phidias?

Phidias is an advanced 3D content generation model that introduces the concept of Retrieval-Enhanced Generation (RAG) to the field of 3D modeling. The model can generate new 3D content based on user-provided 3D reference models or those retrieved from large databases. Phidias improves the quality and controllability of 3D generation tasks through a complex system containing key components such as a meta-control network, dynamic reference routing, and self-reference enhancement. Phidias can generate 3D models from a single image or text prompt, accurately predicting and filling in missing parts of an incomplete 3D model while preserving the detail and integrity of the original model. Phidias supports interactive 3D generation and high-fidelity 3D completion applications, greatly expanding the capabilities and flexibility of 3D modeling.

Phidias' main functions

• Retrieval Enhanced 3D Generation: Generate new 3D content based on retrieved or user-provided 3D reference models.
• Multimodal inputSupports generating 3D content from text, images, and existing 3D models.
• High-quality generationImprove the quality, detail, and realism of the generated 3D models.
• Enhanced generalization abilityBy using a 3D reference model as external memory, the model's ability to handle uncommon perspectives or objects is improved.
• ControllabilityIt allows users to adjust the 3D reference model to control the generation process and achieve the desired 3D shape and style.
• Interactive generationUsers interact with the generated model using rough 3D shape guidance to achieve the desired result.
• High-fidelity completionComplete the missing parts of an incomplete 3D model while preserving the original details.

Phidias's technical principles

• Meta-ControlNet: Dynamically adjust the intensity of the conditional signal to resolve the inconsistency between the reference model and the target image.
• Dynamic Reference RoutingThe resolution of the 3D reference model is adjusted according to different stages of the denoising process, gradually introducing details from coarse to fine.
• Self-reference enhancementUsing an enhanced version of the 3D model as a reference, self-supervised training is performed by simulating various inconsistencies.
• Multi-view diffusion modelConverts a 3D reference model into a multi-view regular coordinate graph (CCM), providing consistent geometric information across different views.
• sparse view 3D reconstructionThe final 3D model is obtained through 3D reconstruction technology based on the generated multi-view images.
• Progressive course learningDuring training, the training difficulty is gradually increased to better utilize reference models with different similarities.

Phidias project address

• Project official websiterag-3d.github.io
• GitHub repository:https://github.com/3DTopia/Phidias-Diffusion
• arXiv technical paper:https://arxiv.org/pdf/2409.11406

Application scenarios of Phidias

• 3D Art and DesignArtists and designers use Phidias to generate 3D models from concept sketches or descriptions, accelerating the creative process.
• Game developmentGame developers can quickly generate game assets, such as characters, items, and environmental elements, using Phidias.
• Film and animation productionIn the film and animation industry, Phidias are used to create highly detailed 3D models, reducing the need for manual modeling.
• Virtual Reality (VR) and Augmented Reality (AR)It can quickly generate realistic 3D objects and scenes for virtual environments, enhancing user immersion.
• Architecture and Urban PlanningGenerate 3D building models based on design sketches or descriptions to aid in planning and visualization.
• Education and trainingIn the field of education, Phidias is used to create instructional models and visualize complex concepts.