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HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance

Junzhe Zhu, Peiye Zhuang, Sanmi Koyejo

arXiv:2305.18766v4cs.CVcs.AIcs.LG

TL;DR

Text-to-3D methods can produce artifacts and view inconsistencies, while NeRFs’ geometry and texture limitations often lead to two-stage optimization. HiFA combines diffusion guidance and NeRF refinements in a single-stage approach; qualitative comparisons report detailed, view-consistent assets and improved appearance over named baselines.

  • Problem

    Existing diffusion-guided text-to-3D methods can yield artifacts and cross-view inconsistencies, while NeRF geometry and texture limitations often prompt two-stage optimization for high-resolution detail.

    Source: §1 · pp. 2–3

  • Method

    HiFA distills diffusion scores in latent and image spaces, anneals timesteps, and adds ray z-variance regularization and kernel smoothing for NeRFs.

    Source: §1 · pp. 2–3

  • Results

    Qualitative comparisons report enhanced photorealism, texture detail, and lighting over Dreamfusion, Magic3D, and Fantasia3D, with multi-view-consistent assets.

    Source: §5.1 · p. 8

  • Takeaways & Limitations

    The paper concludes that combining diffusion guidance with NeRF geometry and sampling refinements supports high-fidelity text-to-3D generation in a single-stage process.

    Source: §6 · p. 9

Abstract

from arXiv · show

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.

1 INTRODUCTION

The introduction proposes improving text-to-3D generation with diffusion guidance and NeRF refinements, while retaining a single-stage NeRF workflow. Its techniques target diffusion supervision, geometry sharpness, and view-dependent texture flicker.

1 INTRODUCTION

  • The approach distills scores in latent and image spaces, and anneals diffusion timesteps to improve text-to-3D generation quality.The timestep decreases inversely with the square root of training iterations; the authors report it outperforms linear and cosine schedules.Source: §1 · pp. 2–3
  • Single-stage NeRF optimization is challenging because NeRFs can produce cloudy geometry and flickering textures, while mesh refinement can forfeit detailed appearances such as fur.Prior approaches use two-stage optimization, extracting textured meshes before a second-stage refinement.Source: §1 · pp. 2–3
  • Z-variance regularization minimizes the variance of sampled ray coordinates, yielding crisper geometry and outperforming alternative spatial regularization.The introduction identifies the alternative as spatial regularization proposed by Barron et al. (2022).Source: §1 · p. 3
  • Kernel smoothing refines coarse-stage importance-sampling weights along NeRF rays, improving surface-region sampling without added computational cost.The method targets texture flicker across views by refining the estimated probability density function before refined-stage sampling.Source: §1 · p. 3
  • The paper presents these diffusion and NeRF modifications as a holistic approach to improving 3D synthesis quality.Its contributions cover diffusion-score distillation, timestep annealing, ray z-variance regularization, and coarse-to-fine kernel smoothing.Source: §1 · p. 3

2 RELATED WORK

Related work moves from unconditional 3D generation toward text-conditioned methods, including approaches guided by CLIP or text-to-image diffusion. The paper builds on diffusion-guided SDS and contrasts its single-stage approach with methods using mesh refinement or other tasks’ pipelines.

2 RELATED WORK

  • Unconditional 3D-asset generation learns asset-data distributions through explicit representations such as point clouds, voxel grids, or meshes, or implicit representations; limited diverse assets led prior work to focus on class-specific and small-scale datasets.Source: §2 · p. 3
  • Text-to-3D methods use either CLIP-based cross-modal matching or 2D diffusion models, and this paper follows the diffusion-guided group.The cited diffusion-guided examples include Poole et al. (2022), Tang et al. (2023), Lin et al. (2023), and Chen et al. (2023b).Source: §2 · p. 3
  • SDS guides NeRF generation with pretrained text-to-image diffusion, while later work varies its formulation or uses two-stage mesh refinement.Wang et al. (2023b) also requires a LoRA trained for each individual 3D asset to provide the score function.Source: §2 · p. 4
  • Image-to-3D reconstruction methods use image-conditioned diffusion or other strategies; some optimize a NeRF before extracting and fine-tuning a mesh.The paper states that it extends its work to image-to-3D reconstruction and compares against these methods in Sec. 5.4.Source: §2 · p. 4

3 PRELIMINARIES: SCORE DISTILLATION SAMPLING (SDS)

The preliminaries describe SDS as diffusion guidance for optimizing a rendered 3D representation. A latent diffusion model encodes rendered images into noisy latents and estimates the noise used to guide optimization.

3 PRELIMINARIES: SCORE DISTILLATION SAMPLING (SDS)

  • SDS uses a pretrained text-to-image diffusion model to guide a 3D representation through rendered images generated from camera poses.The rendered image is produced by a differentiable renderer from the representation parameterized by θ.Source: §3 · p. 4
  • A latent diffusion model encodes an image into a latent, adds timestep-controlled Gaussian noise, and estimates that noise conditioned on the text embedding.The decoder reconstructs an image from a latent, and the SDS loss provides gradients for optimizing the 3D representation.Source: §3 · p. 4
  • The passage identifies ω(t) as a weighting function.Source: §3 · p. 4

4 APPROACH

HiFA combines dual-space score distillation and timestep annealing with NeRF regularization and kernel smoothing to support high-quality text-to-3D generation in a single stage.

4.1 ADVANCING SDS-BASED OPTIMIZATION

  • LSDS+ combines latent-space and image-space reconstruction losses, while image-space supervision alone can cause color bias.The recovered image is decoded from the estimated latent vector.Source: §4.1 · p. 5
  • Square-root timestep annealing uses high noise early and gradually lowers it, with the schedule outperforming alternatives in the reported evaluation.The schedule allocates more iterations to lower timesteps later in training, capturing finer details.Source: §4.1 · p. 6

4.2 ADVANCING REGULARIZATION IN NERF REPRESENTATION

  • Z-variance regularization reduces the variance of sampled ray coordinates to sharpen surfaces and mitigate cloudy geometry.The loss filters out background rays.Source: §4.2 · pp. 6–7
  • Kernel smoothing averages neighboring coarse-stage density weights to broaden refined sampling near high-density regions and reduce flickering.The reported visualization links smoothing with comprehensive peak sampling and view-consistent renderings.Source: §4.2 · p. 7

5 EXPERIMENTS

The experiments compare HiFA with text-to-3D baselines, test its proposed optimization components, and extend evaluation to alternative diffusion guidance and image-to-3D reconstruction.

5.1 EXPERIMENTAL RESULTS

  • HiFA produces photo-realistic, multi-view-consistent assets, with enhanced photo-realism, texture detail, and lighting compared with Dreamfusion, Magic3D, and Fantasia3D.The reported comparisons are qualitative.Source: §5.1 · p. 8

5.2 ABLATION STUDY

  • Image-space regularization improves natural appearance and texture detail, while image-space loss alone produces color bias.Source: §5.2 · p. 8
  • The z-variance loss yields photo-realistic results with crisp geometry; omitting it leads to cloudy artifacts, while distortion loss can compromise appearance detail and hollow geometry.Source: §5.2 · p. 8
  • Square-root timestep annealing captures more detailed appearance and geometry than random, linear, or cosine sampling.Source: §5.2 · p. 9

5.3 OPTIMIZATION WITH GUIDANCE FROM AN ADVANCED TEXT ENCODER

  • Using the stage-1 Deep Floyd IF model addresses SD’s reported difficulty with “doctor” and “jacket” prompts and its multi-face issue; the tested guidance is 64 × 64 resolution.The authors identify use of the full IF model for high-resolution guidance as future work.Source: §5.3 · p. 9, Figure 8 · p. 9

5.4 IMAGE-TO-3D RECONSTRUCTION

  • For single-image reconstruction, HiFA uses 16 hallucinated views, reconstruction loss on given views, and score distillation on remaining sampled views, producing photo-realistic novel views with reasonable detail.The visual comparison includes Zero-1-to-3, Magic123, and SyncDreamer.Source: §5.4 · p. 9, Figure 9 · p. 9

6 CONCLUSION

The conclusion presents a single-stage text-to-3D approach combining diffusion guidance and NeRF improvements for consistent appearance and sharp geometry.

6 CONCLUSION

  • HiFA combines image- and latent-space denoising scores with timestep annealing, z-variance loss, and kernel smoothing for single-stage generation with consistent appearance and sharp geometry.Source: §6 · p. 9

A APPENDIX

The appendix points readers to implementation and training details, additional text-to-3D results and baseline comparisons, and further image-to-3D reconstruction and image-guided hallucination experiments.

A APPENDIX

  • The appendix contains implementation and training details, additional generation results and baseline comparisons, and further image-to-3D reconstruction and image-guided hallucination experiments.Source: A APPENDIX · p. 14

A.1 IMPLEMENTATION DETAILS

The implementation uses an instant-ngp-encoded NeRF with spherical-harmonic ray directions, a directional background network, and pretrained diffusion and disparity models.

A.1 IMPLEMENTATION DETAILS

  • The NeRF uses an MLP with instant-ngp positional encoding, spherical-harmonic ray directions, and a background network that predicts color from ray direction.The setup uses pretrained Stable Diffusion as the diffusion prior and a dense prediction model for disparity maps.Source: §A.1 · p. 14
  • Training uses Adam, 10^4 iterations, 512 × 512 rendering, and DDIM with r = 0.25 and η = 1.The listed loss weights are λrgb = 0.1, λd = 0.1, and λzvar = 3; classifier-free guidance is 100.Source: §A.1 · p. 14

A.2 TRAINING ALGORITHM

Algorithm 1 trains the 3D representation by repeatedly noising a rendered image’s latent, estimating its denoised latent and image, and updating the representation parameters.

A.2 TRAINING ALGORITHM

  • The procedure estimates a denoised latent with single- or multi-step denoising, decodes it into an image, and updates the representation using the loss gradient.Multi-step denoising iteratively denoises the latent until t = 0.Source: §A.2 · p. 14
  • The training loop takes a pre-trained Stable Diffusion model, a rendered image, its latent, a conditioning input, and diffusion-step and noise-schedule settings.The model includes an encoder, decoder, and denoising autoencoder.Source: §A.2 · p. 14
  • Each iteration adds Gaussian noise to the latent according to the diffusion schedule, then estimates the noise using the conditioned denoising model.The noisy latent is formed as z_t = α_t z + σ_t ϵ, with ϵ sampled from a standard normal distribution.Source: §A.2 · p. 14

A.3 ADDITIONAL RESULTS OF TEXT-TO-3D GENERATION

The appendix presents additional generated 3D assets for text prompts in Figures 10–12.

A.3 ADDITIONAL RESULTS OF TEXT-TO-3D GENERATION

  • Figures 10–12 provide more generated 3D assets given text prompts, adding further examples of text-prompt-based 3D asset generation.Source: §A.3 · p. 14

A.4 ADDITIONAL COMPARISONS TO THE BASELINE METHODS

Additional visual comparisons report that HiFA produces detailed, view- and geometry-consistent NeRF results without flickering, and that its components improve ProlificDreamer renderings. The appendix also shows image-to-3D reconstruction and image-guided 3D hallucination results.

A.4 ADDITIONAL COMPARISONS TO THE BASELINE METHODS

  • HiFA produces high-fidelity details and natural colors with NeRF alone, while ProlificDreamer shows flickering and improper geometry; HiFA’s results are view- and geometry-consistent.The comparison follows the first training stage, without baseline fine-tuning.Source: §A.4 · p. 15, Figure 13 · p. 19
  • Additional comparisons include ProlificDreamer, Fantasia3D, Magic3D, and DreamFusion, with some baselines using NeRF followed by fine-tuning.Figures 13–18 provide the visual comparisons.Source: §A.4 · p. 15, Figure 17 · p. 22
  • Adding z-variance loss to ProlificDreamer yields sharper textures, and the full proposed method produces superior renderings with detailed textures.The full method combines z-variance loss, image-space loss, and square-root timestep annealing.Source: §A.4 · p. 15, Figure 19 · p. 24, Figure 20 · p. 24

A.5 ADDITIONAL RESULTS OF IMAGE-TO-3D RECONSTRUCTION

  • The appendix presents additional image-to-3D reconstruction results and image-guided 3D hallucinations from a single image.For hallucination, optimization follows early image-to-3D reconstruction and uses the distillation loss without the image reconstruction loss.Source: §A.5 · p. 15, Figure 21 · p. 24, Figure 22 · p. 25

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