Diffusion-SDF: Text-to-Shape via Voxelized Diffusion (2212.03293v2)

Abstract: With the rising industrial attention to 3D virtual modeling technology, generating novel 3D content based on specified conditions (e.g. text) has become a hot issue. In this paper, we propose a new generative 3D modeling framework called Diffusion-SDF for the challenging task of text-to-shape synthesis. Previous approaches lack flexibility in both 3D data representation and shape generation, thereby failing to generate highly diversified 3D shapes conforming to the given text descriptions. To address this, we propose a SDF autoencoder together with the Voxelized Diffusion model to learn and generate representations for voxelized signed distance fields (SDFs) of 3D shapes. Specifically, we design a novel UinU-Net architecture that implants a local-focused inner network inside the standard U-Net architecture, which enables better reconstruction of patch-independent SDF representations. We extend our approach to further text-to-shape tasks including text-conditioned shape completion and manipulation. Experimental results show that Diffusion-SDF generates both higher quality and more diversified 3D shapes that conform well to given text descriptions when compared to previous approaches. Code is available at: https://github.com/ttlmh/Diffusion-SDF

• The paper introduces a two-stage framework that integrates an SDF autoencoder with a voxelized diffusion model to synthesize high-quality 3D shapes from textual descriptions.
• It employs an innovative UinU-Net architecture to balance local patch details with global structural integrity, enhancing text-guided shape synthesis.
• Empirical results demonstrate improved IoU, CLIP similarity, and effective shape completion, underscoring its potential for advancing 3D modeling applications.

Diffusion-SDF: Advancements in Text-to-Shape Synthesis Through Voxelized Diffusion

The demand for innovative 3D content generation has fueled the development of methodologies that convert textual descriptions into three-dimensional geometries. The paper "Diffusion-SDF: Text-to-Shape via Voxelized Diffusion" introduces a sophisticated framework that addresses the limitations of existing approaches to text-to-shape synthesis. Current methods often suffer from insufficient flexibility in both 3D data representation and the ability to generate diverse 3D shapes that accurately reflect input text descriptors. To overcome these challenges, this paper proposes Diffusion-SDF, a two-stage architecture that integrates various advanced techniques, including diffusion models and autoencoders, tailored for generating voxelized signed distance fields (SDFs).

Core Methodology

The authors articulate a novel approach involving a combination of a signed distance function (SDF) autoencoder and a Voxelized Diffusion Model (VDM). The SDF autoencoder is designed to learn latent representations that retain both local and global structural features of a 3D shape. Notably, it ensures the encoding of voxel grids as patch-independent and Gaussian-distributed entities. The journey begins with a patch-based encoding stage where large 3D models are broken into manageable sub-units, permitting an efficient and focused spatial encoding strategy.

The second stage employs VDM, which utilizes a unique UinU-Net architecture expanding the standard U-Net by incorporating a local-focused inner network. This innovative architecture effectively balances the need to retain local patch structure and integrate global shape integrity. UinU-Net not only processes voxel data efficiently, reducing the time to synthesize complex shapes, but also enhances the generation quality by implementing a finer patch-independent reconstruction mechanism. Furthermore, the integration of a classifier-free guidance mechanism aligned with the diffusion process directs the synthesis pipeline to produce shapes that adhere closely to semantic textual descriptions.

Results and Evaluation

The paper provides compelling empirical results, demonstrating superior performance against existing methods in metrics such as Intersection over Union (IoU), classification accuracy, CLIP similarity, and total mutual difference (TMD). These metrics collectively confirm Diffusion-SDF's capabilities in generating high-quality, semantically accurate, and diverse 3D shapes. Moreover, the approach's competency extends to advanced applications like text-guided shape completion and manipulation, showcasing its versatility. The new shape completion technique using mask-diffusion strategies highlights its proficiency in reconstructing missing sections of known structures under text guidance.

Implications and Future Work

Through its robust framework, Diffusion-SDF not only pushes the boundaries of text-to-shape synthesis but also sets a strategic direction for future research in generative 3D modeling. The proposed dual-stage methodology aligns well with the current push towards bridging the gap between natural language processing and 3D representation learning, a trend that is poised to expand the applications of AI in domains such as animation, AR/VR, and manufacturing design. Importantly, the exploration into text-conditioned 3D generation emphasizes the potential to standardize semantic-driven 3D modeling tools, which could empower non-expert users to create sophisticated 3D content with ease.

Future exploration could focus on integrating additional modalities and refining the existing frameworks to generalize across broader categories beyond the two considered in the Text2Shape dataset. Further attention on reducing sample generation times while maintaining high fidelity could spur real-time applications, representing an intriguing avenue for development in automated design and virtual environment creation.

In summary, "Diffusion-SDF: Text-to-Shape via Voxelized Diffusion" represents a significant advancement in synthesizing high-quality, diversified 3D shapes from textual descriptions, improving the fluidity and feasibility of converting natural language into detailed 3D models through an innovative melding of diffusion models and autoencoders.