SOTAVerified

Text to 3D

Task involves generating 3D objects based on the text prompt provided to the system.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–250 of 314 papers

TitleStatusHype
Plasticine3D: 3D Non-Rigid Editing with Text Guidance by Multi-View Embedding Optimization—0
UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation—0
PI3D: Efficient Text-to-3D Generation with Pseudo-Image Diffusion—0
Mosaic-SDF for 3D Generative Models—0
SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained Geometry and Appearance—0
SceneWiz3D: Towards Text-guided 3D Scene Composition—0
Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D PriorCode1
DreamControl: Control-Based Text-to-3D Generation with 3D Self-PriorCode1
RL Dreams: Policy Gradient Optimization for Score Distillation based 3D Generation—0
Learn to Optimize Denoising Scores for 3D Generation: A Unified and Improved Diffusion Prior on NeRF and 3D Gaussian Splatting—0
SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score DistillationCode1
Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors—0
XCube: Large-Scale 3D Generative Modeling using Sparse Voxel HierarchiesCode2
AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation—0
TPA3D: Triplane Attention for Fast Text-to-3D Generation—0
StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D—0
ControlDreamer: Blending Geometry and Style in Text-to-3D—0
DiverseDream: Diverse Text-to-3D Synthesis with Augmented Text Embedding—0
Adversarial Score Distillation: When score distillation meets GANCode1
LucidDreaming: Controllable Object-Centric 3D Generation—0
X-Dreamer: Creating High-quality 3D Content by Bridging the Domain Gap Between Text-to-2D and Text-to-3D GenerationCode1
GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D GenerationCode2
CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting—0
DreamPropeller: Supercharge Text-to-3D Generation with Parallel SamplingCode1
RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D—0
ET3D: Efficient Text-to-3D Generation via Multi-View Distillation—0
Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion—0
MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D GenerationCode2
GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise—0
LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score MatchingCode2
MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry and Texture—0
DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model—0
Instant3D: Instant Text-to-3D GenerationCode1
One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion—0
Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction ModelCode2
Control3D: Towards Controllable Text-to-3D Generation—0
Consistent4D: Consistent 360° Dynamic Object Generation from Monocular Video—0
Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-trainingCode1
Text-to-3D with Classifier Score Distillation—0
PERF: Panoramic Neural Radiance Field from a Single PanoramaCode2
Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts—0
PaintHuman: Towards High-fidelity Text-to-3D Human Texturing via Denoised Score Distillation—0
GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion ModelsCode2
IPDreamer: Appearance-Controllable 3D Object Generation with Complex Image PromptsCode1
Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraints—0
T^3Bench: Benchmarking Current Progress in Text-to-3D GenerationCode3
SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3DCode2
HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation—0
Text-to-3D using Gaussian SplattingCode2
Progressive Text-to-3D Generation for Automatic 3D PrototypingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ProlificDreamerAvg43.3—Unverified
2Magic3DAvg32.7—Unverified
3LatentNeRFAvg28.1—Unverified
4Fantasia3DAvg24—Unverified
5DreamFusionAvg21.7—Unverified
6SJCAvg18.7—Unverified