Large-Vocabulary 3D Diffusion Model with Transformer (2309.07920v2)

Abstract: Creating diverse and high-quality 3D assets with an automatic generative model is highly desirable. Despite extensive efforts on 3D generation, most existing works focus on the generation of a single category or a few categories. In this paper, we introduce a diffusion-based feed-forward framework for synthesizing massive categories of real-world 3D objects with a single generative model. Notably, there are three major challenges for this large-vocabulary 3D generation: a) the need for expressive yet efficient 3D representation; b) large diversity in geometry and texture across categories; c) complexity in the appearances of real-world objects. To this end, we propose a novel triplane-based 3D-aware Diffusion model with TransFormer, DiffTF, for handling challenges via three aspects. 1) Considering efficiency and robustness, we adopt a revised triplane representation and improve the fitting speed and accuracy. 2) To handle the drastic variations in geometry and texture, we regard the features of all 3D objects as a combination of generalized 3D knowledge and specialized 3D features. To extract generalized 3D knowledge from diverse categories, we propose a novel 3D-aware transformer with shared cross-plane attention. It learns the cross-plane relations across different planes and aggregates the generalized 3D knowledge with specialized 3D features. 3) In addition, we devise the 3D-aware encoder/decoder to enhance the generalized 3D knowledge in the encoded triplanes for handling categories with complex appearances. Extensive experiments on ShapeNet and OmniObject3D (over 200 diverse real-world categories) convincingly demonstrate that a single DiffTF model achieves state-of-the-art large-vocabulary 3D object generation performance with large diversity, rich semantics, and high quality.

• The paper introduces DiffTF, a novel diffusion-based transformer that leverages a distinct triplane representation for efficient and robust 3D object generation.
• The methodology integrates a 3D-aware transformer with an encoder/decoder framework to capture complex geometries and textures across varied categories.
• Experimental validation shows superior performance with lower FID and KID scores, confirming the model's ability to generate realistic, diverse 3D assets.

Analysis of "Large-Vocabulary 3D Diffusion Model with Transformer"

The paper "Large-Vocabulary 3D Diffusion Model with Transformer" presents a novel approach to 3D object generation using a diffusion-based model. This research addresses three fundamental challenges in large-vocabulary 3D generation: the need for efficient 3D representation, diverse geometry and texture across categories, and the complexity of real-world object appearances. The proposed model, DiffTF, leverages a triplane-based 3D representation coupled with a transformer architecture to synthesize diverse 3D objects from a wide range of categories with a single generative model.

Key Components and Methodologies

The authors introduce Distinct Triplane Representation as the underlying framework for efficient 3D object modeling. Unlike conventional representations, this triplane approach enhances both the robustness and accuracy of 3D feature fitting, improving convergence through normalization and strong regularization.

3D-Aware Transformer: At the core of the DiffTF architecture is a 3D-aware transformer, proposed to tackle the extensive variability in geometry and texture. This transformer integrates cross-plane attention to capture generalized 3D knowledge, contributing to the model's capability to handle diverse categories by assimilating such knowledge with specialized 3D object features.

3D-Aware Encoder/Decoder: To improve handling of complex real-world appearances, a 3D-aware encoder/decoder framework is implemented to fortify the encoded triplanes with generalized 3D knowledge.

Experimental Validation and Results

The performance of the DiffTF model was empirically validated against state-of-the-art generative models like DiffRF and NFD on large datasets such as ShapeNet and OmniObject3D. The paper claims a superior performance in terms of generating 3D objects with diverse and complex geometry, achieving high quality and semantic consistency throughout a diverse array of categories.

Strong numerical results are presented, indicating the DiffTF model achieves lower Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) scores, with enhanced Coverage Score (COV) and Minimum Matching Distance (MMD) values for generational accuracy and diversity. These metrics highlight the model's capacity to create detailed, lifelike 3D textures and shapes, situating it ahead of other contemporary methods.

Theoretical and Practical Implications

From a theoretical perspective, the development of a diffusion-based transformer model supporting large-vocabulary 3D generation establishes a significant shift from category-specific 3D generation practices. The work integrates both high-level semantic understanding and intricate detail representation, paving the way for highly scalable models in the field of 3D object generation.

Practically, the DiffTF framework could significantly impact applications where robustness across diverse 3D object categories is critical—such as virtual reality, gaming, and animation industries, where realistic, dynamic asset generation remains a core component.

Future Directions

The authors acknowledge limitations in triplane fitting speed and detail richness for complex categories—areas for potential future improvement. Further developments might focus on enhancing efficiency, implementing real-time generation capabilities, and extending applicability to even broader categories. Beyond these immediate enhancements, exploring the ramifications of combining DiffTF with other modalities (e.g., integrating physical simulation with object generation) could yield innovative tools for synthetic data creation across multiple disciplines.

In conclusion, this paper presents substantive advancements in 3D generative models, marked by a purposeful integration of diffusion processes within a transformer-based framework, and it stands as a foundation for expansive research on versatile and scalable 3D object generation technologies.