MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation (2311.14494v2)

Abstract: We introduce MVControl, a novel neural network architecture that enhances existing pre-trained multi-view 2D diffusion models by incorporating additional input conditions, e.g. edge maps. Our approach enables the generation of controllable multi-view images and view-consistent 3D content. To achieve controllable multi-view image generation, we leverage MVDream as our base model, and train a new neural network module as additional plugin for end-to-end task-specific condition learning. To precisely control the shapes and views of generated images, we innovatively propose a new conditioning mechanism that predicts an embedding encapsulating the input spatial and view conditions, which is then injected to the network globally. Once MVControl is trained, score-distillation (SDS) loss based optimization can be performed to generate 3D content, in which process we propose to use a hybrid diffusion prior. The hybrid prior relies on a pre-trained Stable-Diffusion network and our trained MVControl for additional guidance. Extensive experiments demonstrate that our method achieves robust generalization and enables the controllable generation of high-quality 3D content. Code available at https://github.com/WU-CVGL/MVControl/.

Overview of "MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation"

The paper introduces MVControl, a novel architecture designed to enhance multi-view diffusion models for controllable text-to-3D generation. The core innovation lies in integrating conditional controls into existing pre-trained models to enable the generation of high-fidelity, view-consistent 3D content guided by additional inputs such as edge maps.

Methodological Contributions

The authors build upon the multi-view diffusion model MVDream and propose an additional neural network module as a plugin to learn task-specific conditions. The new conditioning mechanism predicts embeddings representing input spatial and view conditions and injects them into the network globally. This design provides precise control over the shapes and views of generated images.

A notable feature of MVControl is its incorporation into the score-distillation (SDS) loss-based optimization process to generate 3D content. The hybrid diffusion prior, a combination of a pre-trained Stable-Diffusion network and MVControl, serves as additional guidance.

Experimental Validation

The paper shows extensive experiments demonstrating MVControl's capacity to generate controllable, high-quality 3D content. The results indicate robust generalization and refinement in the generated assets' fidelity compared to existing methods.

Theoretical and Practical Implications

MVControl suggests a new direction for integrating additional conditions into diffusion models for enhanced generation control. The system enables substantial advancements in the quality and controllability of text-to-image synthesis extended into the 3D domain.

Practically, the innovation could significantly impact 3D asset creation, permitting more refined control over the generation process through user-defined conditions. The approach facilitates applications across various fields, including virtual reality, gaming, and design.

Future Developments

The paper opens avenues for further research into multi-condition controls across diverse input types, such as depth maps and sketches. Adaptations and refinements of the proposed network could lead to broader applications in general 3D vision and graphics.

In conclusion, MVControl represents a significant methodological advancement in controllable 3D content generation, achieving high fidelity and control through a well-defined conditioning mechanism and integration with existing diffusion technologies. The approach not only enhances current methodologies but also lays a foundation for future innovations in AI-driven design and modeling.