Semantic Score Distillation Sampling for Compositional Text-to-3D
  Generation
Authors: name: Ling Yang, name: Zixiang Zhang, name: Junlin Han, name: Bohan Zeng, name: Runjia Li, name: Philip Torr, name: Wentao Zhang

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