Consistent Flow Distillation for Text-to-3D Generation (2501.05445v1)

Abstract: Score Distillation Sampling (SDS) has made significant strides in distilling image-generative models for 3D generation. However, its maximum-likelihood-seeking behavior often leads to degraded visual quality and diversity, limiting its effectiveness in 3D applications. In this work, we propose Consistent Flow Distillation (CFD), which addresses these limitations. We begin by leveraging the gradient of the diffusion ODE or SDE sampling process to guide the 3D generation. From the gradient-based sampling perspective, we find that the consistency of 2D image flows across different viewpoints is important for high-quality 3D generation. To achieve this, we introduce multi-view consistent Gaussian noise on the 3D object, which can be rendered from various viewpoints to compute the flow gradient. Our experiments demonstrate that CFD, through consistent flows, significantly outperforms previous methods in text-to-3D generation.

• The paper presents Consistent Flow Distillation (CFD) to overcome SDS limitations by ensuring multi-view consistency in text-to-3D generation.
• It utilizes a multi-view consistent Gaussian noise mechanism to guide coherent gradient computations, enhancing both quality and diversity of 3D assets.
• Experimental and theoretical analyses confirm that CFD yields more realistic 3D models, with promising applications in VR, gaming, and digital content creation.

Consistent Flow Distillation for Text-to-3D Generation

The paper "Consistent Flow Distillation for Text-to-3D Generation" introduces a novel approach to address challenges in text-to-3D generation using diffusion models. The core contribution lies in proposing Consistent Flow Distillation (CFD), which refines the process of transferring 2D generative knowledge to 3D generation by emphasizing consistency and diversity in the created 3D assets.

Key Contributions and Methodology

1. Consistent Flow Distillation (CFD): CFD ameliorates the limitations observed in Score Distillation Sampling (SDS), such as visual degradation and limited diversity due to its maximum-likelihood-seeking behavior. CFD leverages the gradient of diffusion processes to guide 3D generation by maintaining consistency in 2D image flows across multiple viewpoints. This consistency is crucial for producing high-quality 3D representations.
2. Multi-View Consistent Gaussian Noise: CFD introduces a multi-view consistent Gaussian noise mechanism. This innovation ensures that the noise applied to the 3D object is consistent across different views, enabling coherent gradient computations for better 3D object generation. This multi-view consistency acts akin to maintaining a fixed noise pattern for specific regions, ensuring visual constancy across varying perspectives.
3. Theoretical Framework: Theoretical analyses are provided for the application of deterministic and stochastic diffusion sampling processes in 3D generation. The formulation extends existing paradigms and highlights the essential role of flow consistency in achieving superior results compared to traditional SDS methodologies.
4. Experimental Results: The proposed method outperforms existing techniques in terms of visual fidelity and diversity. Extensive qualitative and quantitative experiments demonstrate that CFD generates more realistic and diverse 3D assets.

Implications and Future Directions

CFD offers a promising advancement in the field of text-to-3D model distillation, addressing significant limitations of previous methodologies. The focus on multi-view consistency can inspire further research into enhancing 3D generation processes through similar consistency-based techniques.

Practical Implications: By improving the quality and diversity of generated 3D models, CFD has potential applications in fields like virtual reality, gaming, and digital content creation, where realistic and varied 3D assets are essential.

Theoretical Implications: The introduction of consistent flows aligns with broader efforts in the field to harmonize the dynamics of 2D and 3D generative models, suggesting avenues for developing unified frameworks that can efficiently transition between these dimensions.

Future Research: Potential developments could explore further integration of CFD with other generative models like GANs or variational autoencoders, or investigate its application in non-static scenes where dynamic object generation might be required.

In summary, the paper presents a novel contribution to the improvement of 3D generative techniques from text prompts, combining theoretical insight with practical efficacy in generative quality and diversity enhancement.