MVLight: Enhancing Text-to-3D Generation with Light-Conditioned Multi-View Diffusion for Improved Relighting
MVLight, a novel light-conditioned multi-view diffusion model, enhances text-to-3D generation by integrating lighting conditions directly into the generation process, resulting in higher-quality 3D models with improved geometric precision and superior relighting capabilities compared to existing methods.
DreamPolish: A Novel Text-to-3D Generation Model for Enhanced Geometry and Texture
DreamPolish is a novel text-to-3D generation model that leverages progressive geometry generation and domain score distillation to produce 3D objects with refined geometry and high-quality, photorealistic textures, outperforming existing state-of-the-art methods.
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.
