A Comprehensive Survey of Text-to-3D Generation in the Age of AI-Generated Content
This paper surveys the rapidly developing field of text-to-3D generation, exploring core technologies, seminal methods, enhancement directions, and applications, ultimately highlighting its potential to revolutionize 3D content creation.
Layout-Your-3D: Controllable and Precise 3D Generation with 2D Blueprints Using Efficient Reconstruction Models and a Two-Step Refinement Process
Layout-Your-3D enables efficient and controllable generation of complex 3D scenes from text prompts by leveraging 2D layouts as blueprints, outperforming existing methods in speed and accuracy.
JointDreamer: A Novel Framework for Text-to-3D Generation Addressing Geometric Inconsistency in Score Distillation Sampling
JointDreamer introduces Joint Score Distillation (JSD), a novel method that enhances 3D consistency in text-to-3D generation by modeling inter-view coherence, effectively addressing the limitations of traditional Score Distillation Sampling (SDS).
Semantic Score Distillation Sampling (SemanticSDS) for Enhanced Text-to-3D Generation of Compositional Scenes
Semantic Score Distillation Sampling (SemanticSDS) improves compositional text-to-3D generation by incorporating semantic embeddings and region-specific denoising, enabling the creation of complex scenes with multiple, detailed objects and interactions.
SeMv-3D: Achieving Semantic and Multi-view Consistency in General Text-to-3D Generation Using Triplane Priors
SeMv-3D is a novel framework that leverages triplane priors and a two-step learning process to generate semantically consistent and multi-view coherent 3D objects from text descriptions.
Efficient Refinement of Coarse 3D Assets into High-Quality Text-Guided 3D Generation using Multi-View Diffusion
BoostDream, a novel method that seamlessly combines differentiable rendering and text-to-image advancements, can efficiently refine coarse 3D assets into high-quality 3D content guided by text prompts.
Generating High-Quality Textured 3D Meshes from Text Prompts with DreamMesh
DreamMesh is a novel text-to-3D generation framework that leverages explicit 3D representation of triangle meshes to produce high-quality 3D models with clean geometry and rich texture details.
Efficient Text-Guided 3D Textured Mesh Generation with Triplane Attention
TPA3D, a GAN-based deep learning framework, can efficiently generate high-quality 3D textured meshes that closely align with detailed text descriptions, without relying on human-annotated text-3D pairs for training.
DreamMapping: Efficient High-Fidelity Text-to-3D Generation via Variational Distribution Mapping
DreamMapping introduces a novel Variational Distribution Mapping (VDM) approach that efficiently models the distribution of rendered images by treating them as degraded versions of diffusion model outputs. This, combined with a Distribution Coefficient Annealing (DCA) strategy, enables the generation of high-fidelity and realistic 3D assets from text prompts.
Improving Text-to-3D Content Creation with Time Prioritized Score Distillation Sampling
The proposed DreamTime method significantly improves the quality and diversity of text-to-3D content generation by aligning the 3D optimization process with the sampling process of pre-trained diffusion models.