Semantic Score Distillation Sampling for Compositional Text-to-3D Generation
Region-specific semantic guidance creates photorealistic 3D scenes with precise object placement and interactions
Region-specific semantic guidance creates photorealistic 3D scenes with precise object placement and interactions
Original Problem 🔍:
Generating high-quality, complex 3D scenes with multiple objects or intricate interactions remains challenging in text-to-3D generation. Existing methods struggle with fine-grained control and expressiveness.
Solution in this Paper 🛠️:
• Introduces Semantic Score Distillation Sampling (SEMANTICSDS)
• Utilizes program-aided layout planning for accurate object positioning
• Develops expressive semantic embeddings for 3D Gaussian representations
• Transforms semantic embeddings into a semantic map for region-specific SDS
• Implements object-specific view descriptors for global scene optimization
Key Insights from this Paper 💡:
• Explicit semantic guidance unlocks compositional capabilities of pre-trained diffusion models
• Semantic embeddings maintain consistency across different rendering views
• Region-wise SDS process enables precise optimization and compositional generation
• Object-specific view descriptors improve multi-view understanding