SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulation (2303.12236v2)

Abstract: We present a cascaded diffusion model based on a part-level implicit 3D representation. Our model achieves state-of-the-art generation quality and also enables part-level shape editing and manipulation without any additional training in conditional setup. Diffusion models have demonstrated impressive capabilities in data generation as well as zero-shot completion and editing via a guided reverse process. Recent research on 3D diffusion models has focused on improving their generation capabilities with various data representations, while the absence of structural information has limited their capability in completion and editing tasks. We thus propose our novel diffusion model using a part-level implicit representation. To effectively learn diffusion with high-dimensional embedding vectors of parts, we propose a cascaded framework, learning diffusion first on a low-dimensional subspace encoding extrinsic parameters of parts and then on the other high-dimensional subspace encoding intrinsic attributes. In the experiments, we demonstrate the outperformance of our method compared with the previous ones both in generation and part-level completion and manipulation tasks.

• The paper introduces SALAD, a novel approach leveraging part-level latent diffusion to achieve high-quality 3D shape generation and manipulation.
• It utilizes a transformer network with AdaLN layers to capture distinct part features and ensure consistency across diverse shape classes.
• Experimental results demonstrate improved FID scores and robust zero-shot, text-guided refinement capabilities, advancing digital design and VR applications.

SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulation

The paper "SALAD: Part-Level Latent Diffusion for 3D Shape Generation and Manipulation" introduces a novel approach in the domain of 3D shape generation utilizing cascaded diffusion models trained on part-level 3D representations. The method, SALAD, focuses on achieving high-quality shape generation and demonstrating zero-shot capabilities in various manipulation scenarios such as part mixing, refinement, and text-guided part completion. SALAD stands for Shape-Abstraction Latent Diffusion, emphasizing its unique focus on handling part-level representations within the 3D shape synthesis context.

Methodology Overview

The methodology builds upon principles from latent diffusion model literature, integrating them into both stages of shape generation and manipulation. SALAD comprises two main phases: firstly, the latent space representation of 3D shape parts is streamlined through Gaussian parameterization, followed by the second stage of enforcing consistency in part completion or manipulation driven by diffusion processes. By adopting a product space encompassing orthogonal and Euclidean groups, SALAD advances the application of diffusion models in the geometrical domain, specifically addressing the challenges of modeling rotational and spatial distributions simultaneously.

The SALAD approach employs a transformer-based network integrated with AdaLN layers, which assists in capturing part-specific features efficiently, ensuring the part representations are both distinct and consistent across multiple shape classes. Training employs batch normalization and polynomial decay learning rates to stabilize convergence given SALAD's intrinsic complexity.

Experimental Results

The paper presents empirical evaluations on multiple fronts, demonstrating SALAD's proficiency across shape generation, completion, and text-based inference. SALAD has shown significant improvements in quality compared to baseline models like DPM, PVD, LION, and others, notably in challenging shape classes such as chairs and airplanes. Quantitative results highlighted through metrics like FID scores, indicate SALAD's superiority in generating diverse and semantically coherent shapes from minimal data sets—a crucial advancement in efficient 3D artistic design and modeling workflows.

Furthermore, results from text-guided manipulation experiments reveal enhanced flexibility and interpretability in generating shapes that accurately align with descriptive prompts. By integrating LSTM-based encoder mechanisms into SALAD, the model can adaptively refine or complete shape parts given textual descriptions, showing remarkable textual inference capabilities.

Implications and Future Directions

The implications of this research are considerable, especially within fields that leverage 3D modeling such as architecture, gaming, and virtual reality. SALAD presents opportunities for automated, high-fidelity digital content creation, allowing users to manipulate shapes through simple textual descriptions. The zero-shot capabilities further suggest potential for broader applications across unseen classes without explicit retraining.

Theoretically, the work posits interesting directions for future exploration. For instance, extending SALAD's framework to learn in fewer epochs or apply to even larger datasets with nuanced categories could reveal novel insights into diffusion-based generative modeling. Incorporating more complex text encoders like transformers might enhance contextual understanding, allowing for more nuanced and varied manipulations. Additionally, exploring multi-modal integrations, such as combining SALAD with image inputs, could provide richer interaction pathways for comprehensive media synthesis.

The paper lays foundational work in expanding latent diffusion approaches within 3D shape generation and paves the way for enhanced, user-friendly creative technologies that couple robust modeling techniques with intuitive interfaces.