Semantic Score Distillation Sampling for Compositional Text-to-3D Generation (2410.09009v1)

Abstract: Generating high-quality 3D assets from textual descriptions remains a pivotal challenge in computer graphics and vision research. Due to the scarcity of 3D data, state-of-the-art approaches utilize pre-trained 2D diffusion priors, optimized through Score Distillation Sampling (SDS). Despite progress, crafting complex 3D scenes featuring multiple objects or intricate interactions is still difficult. To tackle this, recent methods have incorporated box or layout guidance. However, these layout-guided compositional methods often struggle to provide fine-grained control, as they are generally coarse and lack expressiveness. To overcome these challenges, we introduce a novel SDS approach, Semantic Score Distillation Sampling (SemanticSDS), designed to effectively improve the expressiveness and accuracy of compositional text-to-3D generation. Our approach integrates new semantic embeddings that maintain consistency across different rendering views and clearly differentiate between various objects and parts. These embeddings are transformed into a semantic map, which directs a region-specific SDS process, enabling precise optimization and compositional generation. By leveraging explicit semantic guidance, our method unlocks the compositional capabilities of existing pre-trained diffusion models, thereby achieving superior quality in 3D content generation, particularly for complex objects and scenes. Experimental results demonstrate that our SemanticSDS framework is highly effective for generating state-of-the-art complex 3D content. Code: https://github.com/YangLing0818/SemanticSDS-3D

Semantic Score Distillation Sampling for Compositional Text-to-3D Generation

The paper "Semantic Score Distillation Sampling for Compositional Text-to-3D Generation" addresses the challenge of generating high-quality 3D assets from textual descriptions—a notable task in computer graphics and vision. This research introduces Semantic Score Distillation Sampling (SemanticSDS) as an innovative approach to enhance the expressiveness and accuracy of text-to-3D generation using pre-trained 2D diffusion priors.

Technical Contributions

The authors identify key limitations in existing text-to-3D generation methods, notably the reliance on coarse layout guidance that fails to provide fine-grained control over the generation process. SemanticSDS proposes a novel solution by integrating semantic embeddings that retain consistency across different views and clearly distinguish various objects and parts within a scene.

Key components of this methodology include:

1. Program-Aided Layout Planning: This step enhances LLM-based layout planning by introducing structured languages and programmable reasoning to derive precise 3D coordinates for scene composition. It addresses spatial arrangement issues by employing a program as an intermediary to improve the translation of vague textual descriptions into precise layouts.
2. Semantic Embeddings: The paper employs a semantic map derived from new embeddings that direct the region-specific Score Distillation Sampling (SDS) process. This allows fine-grained optimization and compositional generation, improving the pre-trained diffusion models' utilisation for complex scenes with multiple objects.
3. Object-Specific View Descriptor: Designed to overcome the Janus Problem during global scene optimization, this component assigns specific view descriptors to each object. This ensures coherent multi-view scene generation, leading to better scene quality and visual harmony.

Experimental Evaluation

The experimental results indicate that SemanticSDS significantly advances the state-of-the-art in complex 3D content generation. Quantitative metrics such as CLIP Score and evaluations from GPT-4V demonstrate superior scene quality, prompt alignment, and geometric fidelity compared to methods like GALA3D and GraphDreamer.

• CLIP Score: SemanticSDS shows enhanced alignment with the primary semantics of user prompts.
• Human-Aligned Evaluation: Utilizing GPT-4V, SemanticSDS outperforms baseline methods on multiple criteria, including spatial arrangement and scene quality.

The user paper further corroborates these findings, as participants consistently prefer the outputs generated by SemanticSDS over competing methods.

Implications and Future Directions

The implications of this research are multifaceted:

• Practical: SemanticSDS offers a framework for high-quality 3D content generation that can be applied to various fields, including virtual reality, animation, and gaming.
• Theoretical: The integration of semantic guidance into the SDS framework showcases a novel application of pre-trained 2D diffusion models in a 3D context, paving the way for future research in compositional generation.

Looking forward, SemanticSDS could be expanded to incorporate automatic editing and closed-loop refinement, potentially influencing a broader range of applications in AI-driven creative processes.

In conclusion, the development and implementation of SemanticSDS present a noteworthy advancement in text-to-3D generation by resolving existing challenges through innovative techniques. This work not only enhances the compositional capabilities of diffusion models but also sets the groundwork for future explorations in semantic-guided generation in AI.