HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance (2305.18766v4)

Abstract: The advancements in automatic text-to-3D generation have been remarkable. Most existing methods use pre-trained text-to-image diffusion models to optimize 3D representations like Neural Radiance Fields (NeRFs) via latent-space denoising score matching. Yet, these methods often result in artifacts and inconsistencies across different views due to their suboptimal optimization approaches and limited understanding of 3D geometry. Moreover, the inherent constraints of NeRFs in rendering crisp geometry and stable textures usually lead to a two-stage optimization to attain high-resolution details. This work proposes holistic sampling and smoothing approaches to achieve high-quality text-to-3D generation, all in a single-stage optimization. We compute denoising scores in the text-to-image diffusion model's latent and image spaces. Instead of randomly sampling timesteps (also referred to as noise levels in denoising score matching), we introduce a novel timestep annealing approach that progressively reduces the sampled timestep throughout optimization. To generate high-quality renderings in a single-stage optimization, we propose regularization for the variance of z-coordinates along NeRF rays. To address texture flickering issues in NeRFs, we introduce a kernel smoothing technique that refines importance sampling weights coarse-to-fine, ensuring accurate and thorough sampling in high-density regions. Extensive experiments demonstrate the superiority of our method over previous approaches, enabling the generation of highly detailed and view-consistent 3D assets through a single-stage training process.

• The paper introduces a single-stage text-to-3D generation framework that replaces traditional two-stage processes with advanced diffusion guidance.
• It enhances Score Distillation Sampling by incorporating latent and image space supervision and employs timestep annealing to progressively boost generation quality.
• NeRF regularization improvements via Z-variance reduction and kernel smoothing minimize artifacts, yielding sharper surfaces and consistent textures.

High-fidelity Text-to-3D Generation with HiFA

The paper "HiFA: High-fidelity Text-to-3D generation with Advanced Diffusion Guidance" addresses an important challenge in the field of automatic text-to-3D asset generation, an area with growing applications in digital content creation, virtual reality, and more. One of the central issues in existing methods is the artifacts and inconsistencies caused by suboptimal optimization strategies, particularly when using pre-trained text-to-image diffusion models in conjunction with Neural Radiance Fields (NeRFs).

Key Contributions

The authors propose a series of novel techniques aimed at producing high-quality text-to-3D generations in a single optimization stage, effectively bypassing the limitations seen in previous two-stage processes. The innovations are introduced along two main dimensions—advancements in Score Distillation Sampling (SDS) and improvements in regularization methodologies for NeRF representations.

Score Distillation Sampling (SDS) Enhancements:

• Latent and Image Space Supervision: The paper adapts the SDS methodology to include both latent and image space loss components within the pre-trained diffusion models, maximizing the 3D representation's fidelity to the textual description.
• Timestep Annealing: Instead of randomly sampling noise levels (timesteps) during training, a novel timestep annealing approach correlates these with training iterations, progressively enhancing the generation quality as training progresses. Empirical results demonstrate the superiority of this square root annealing method over linear and cosine alternatives.

NeRF Regularization Improvements:

• Z-Variance Regularization: This technique reduces variance in z-coordinate sampling along NeRF rays, which results in crisper geometry surfaces without the need for secondary optimization stages that typically lead to unwanted tradeoffs like in mesh-based strategies.
• Kernel Smoothing Technique: A novel smoothing technique refines the probability density function estimation during importance sampling, significantly reducing texture flickering across viewing angles, thus ensuring consistent appearance.

Implications

The methodologies proposed have profound implications for the field of 3D asset generation:

1. Enhanced Single-Stage Optimization: The approach negates the need for iterative stages, which have typically been necessary for achieving high-resolution details, thus streamlining the training process.
2. Improved Computational Efficiency: By eliminating the need for complex representations or two-stage processes, computational resources are more effectively utilized, facilitating broader application across different domains.
3. Greater Versatility in 3D Representation: The flexibility to retain photo-realism and intricate detail in NeRFs could pave the way for more diverse applications in both commercial and artistic domains.

Future Directions

The paper suggests significant potential for future development in the area of AI-driven 3D generation. One interesting pathway is the potential use of fully integrated models that utilize comprehensive latent space guidance for enhanced fidelity. Furthermore, the adaptive nature of the techniques could be extended to other generative tasks, such as image-to-3D reconstruction and conditional asset modification, paving the way for more dynamic interactive content experiences.

Overall, this work stands as a testament to the potential embedded in refining and integrating advanced diffusion models into existing 3D generation frameworks. The insights presented provide new approaches for tackling the longstanding difficulties associated with automatic 3D generation from textual descriptions, offering a more nuanced understanding of the interplay between image rendering and geometric consistency.