Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping

Zijie Pan, Jiachen Lu, Xiatian Zhu, Li Zhang

TL;DR

This work tackles the challenge of high-resolution 3D texture synthesis when annotated data are scarce, identifying unregulated pixel-wise gradients from the latent image pathway as a key bottleneck. It introduces Pixel-wise Gradient Clipping (PGC), a lightweight, plug-in technique that regulates the magnitude of pixel-wise gradients while preserving texture-relevant directions, and integrates seamlessly with Score Distillation Sampling and Latent Diffusion Model frameworks. The authors also propose PNGD as a complementary gradient-regulation strategy, along with a noise-bounded assumption and a controllable latent-gradient mechanism using depth/normal guidance. Empirical results across mesh optimization tasks and multiple SDS/LDM pipelines demonstrate consistent texture-quality improvements and robust performance, supported by a user preference study that favors the proposed approach. The method offers practical impact by enabling higher-fidelity 3D texture synthesis with minimal computational overhead and broad compatibility with existing high-resolution 3D generation pipelines.

Abstract

High-resolution 3D object generation remains a challenging task primarily due to the limited availability of comprehensive annotated training data. Recent advancements have aimed to overcome this constraint by harnessing image generative models, pretrained on extensive curated web datasets, using knowledge transfer techniques like Score Distillation Sampling (SDS). Efficiently addressing the requirements of high-resolution rendering often necessitates the adoption of latent representation-based models, such as the Latent Diffusion Model (LDM). In this framework, a significant challenge arises: To compute gradients for individual image pixels, it is necessary to backpropagate gradients from the designated latent space through the frozen components of the image model, such as the VAE encoder used within LDM. However, this gradient propagation pathway has never been optimized, remaining uncontrolled during training. We find that the unregulated gradients adversely affect the 3D model's capacity in acquiring texture-related information from the image generative model, leading to poor quality appearance synthesis. To address this overarching challenge, we propose an innovative operation termed Pixel-wise Gradient Clipping (PGC) designed for seamless integration into existing 3D generative models, thereby enhancing their synthesis quality. Specifically, we control the magnitude of stochastic gradients by clipping the pixel-wise gradients efficiently, while preserving crucial texture-related gradient directions. Despite this simplicity and minimal extra cost, extensive experiments demonstrate the efficacy of our PGC in enhancing the performance of existing 3D generative models for high-resolution object rendering.

Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping

TL;DR

This work tackles the challenge of high-resolution 3D texture synthesis when annotated data are scarce, identifying unregulated pixel-wise gradients from the latent image pathway as a key bottleneck. It introduces Pixel-wise Gradient Clipping (PGC), a lightweight, plug-in technique that regulates the magnitude of pixel-wise gradients while preserving texture-relevant directions, and integrates seamlessly with Score Distillation Sampling and Latent Diffusion Model frameworks. The authors also propose PNGD as a complementary gradient-regulation strategy, along with a noise-bounded assumption and a controllable latent-gradient mechanism using depth/normal guidance. Empirical results across mesh optimization tasks and multiple SDS/LDM pipelines demonstrate consistent texture-quality improvements and robust performance, supported by a user preference study that favors the proposed approach. The method offers practical impact by enabling higher-fidelity 3D texture synthesis with minimal computational overhead and broad compatibility with existing high-resolution 3D generation pipelines.

Abstract

High-resolution 3D object generation remains a challenging task primarily due to the limited availability of comprehensive annotated training data. Recent advancements have aimed to overcome this constraint by harnessing image generative models, pretrained on extensive curated web datasets, using knowledge transfer techniques like Score Distillation Sampling (SDS). Efficiently addressing the requirements of high-resolution rendering often necessitates the adoption of latent representation-based models, such as the Latent Diffusion Model (LDM). In this framework, a significant challenge arises: To compute gradients for individual image pixels, it is necessary to backpropagate gradients from the designated latent space through the frozen components of the image model, such as the VAE encoder used within LDM. However, this gradient propagation pathway has never been optimized, remaining uncontrolled during training. We find that the unregulated gradients adversely affect the 3D model's capacity in acquiring texture-related information from the image generative model, leading to poor quality appearance synthesis. To address this overarching challenge, we propose an innovative operation termed Pixel-wise Gradient Clipping (PGC) designed for seamless integration into existing 3D generative models, thereby enhancing their synthesis quality. Specifically, we control the magnitude of stochastic gradients by clipping the pixel-wise gradients efficiently, while preserving crucial texture-related gradient directions. Despite this simplicity and minimal extra cost, extensive experiments demonstrate the efficacy of our PGC in enhancing the performance of existing 3D generative models for high-resolution object rendering.
Paper Structure (30 sections, 12 equations, 11 figures)

This paper contains 30 sections, 12 equations, 11 figures.

Table of Contents

  1. Introduction
  2. Related work
  3. 2D diffusion models
  4. 3D shape and texture generation using 2D diffusion
  5. Gradient clipping/normalizing techniques
  6. Background
  7. Score distillation sampling (SDS)
  8. Parameter-wise normalized gradient descent and gradient clipping
  9. Method
  10. VAE optimization regulation
  11. Score Distillation Sampling process regulation
  12. Pixel-wise normalized gradient descent (PNGD)
  13. Pixel-wise gradient clipping (PGC)
  14. Noise assumption
  15. Controllable latent gradients
  16. ...and 15 more sections

Figures (11)

  • Figure 1: Blender rendering for textured meshes.Top: Fantasia3D chen2023fantasia3d. Bottom: Ours. For each mesh in the top, we can find a corresponding one in the bottom whose texture is generated conditioned on the same prompt. Our method generates more detailed and realistic texture and exhibits better consistency with input prompts.
  • Figure 2: Visualization of 2D/3D results and typical gradients guided by different LDMs.(A) Stable Diffusion 2.1-base rombach2022high as guidance. (B) SDXL podell2023sdxl as guidance. The text prompt is a wooden car. For each case, we visualize (a) directly optimizing a 2D image using SDS loss, alongside (b) the corresponding gradients; (c) optimizing a texture field chen2023fantasia3d based on a fixed mesh of car. We compare six gradient propagation methods: (i) Backpropagation of latent gradients, (ii) VAE gradients, (iii) linear approximated VAE gradients, (iv) normalized VAE gradients, (v) our proposed PGC VAE gradients by value and (vi) by norm. $\bigcirc$ highlights gradient noise.
  • Figure 3: Comparison with baselines. With the meshes fixed, we compare 4 methods: Fantasia3D chen2023fantasia3d, Fantasia3D+SDXL podell2023sdxl, Fantasia3D+PGC and Fantasia3D+SDXL+PGC (Ours).
  • Figure 4: Comparison of using normal-SDS jointly with RGB-SDS. We compare 5 methods: Fantasia3D chen2023fantasia3d, Fantasia3D+SDXL podell2023sdxl, Fantasia3D+PGC, Fantasia3D+SDXL+PGC (Ours) and Fantasia3D+SDXL+PGC w/o normal-SDS (Ours w/o nrm).
  • Figure 5: PGC can benefit various pipelines, including Stable-Dreamfusion stable-dreamfusion , Fantasia3D chen2023fantasia3d geometry stage and Zero123 liu2023zero.
  • ...and 6 more figures