https://github.com/lutao2021/BrightDreamer
공식 코드BrightDreamer: Generic 3D Gaussian Generative Framework for Fast Text-to-3D Synthesis
Papers with Code 아카이브
3D Generation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/magic-research/magic-boost
공식 코드Magic-Boost: Boost 3D Generation with Multi-View Conditioned Diffusion
https://github.com/ming1993li/instant3dcodes
공식 코드https://github.com/ml-gsai/microdreamer
공식 코드MicroDreamer: Efficient 3D Generation in $\sim$20 Seconds by Score-based Iterative Reconstruction
https://github.com/ncsoft/CaPa
공식 코드CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation
https://github.com/nirvanalan/ln3diff
공식 코드LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
https://github.com/nv-tlabs/LION
공식 코드https://github.com/octree-nn/octfusion
공식 코드OctFusion: Octree-based Diffusion Models for 3D Shape Generation
https://github.com/ohad204/Lay-A-Scene
공식 코드Lay-A-Scene: Personalized 3D Object Arrangement Using Text-to-Image Priors
https://github.com/onethousand1000/animportrait3d
공식 코드Text-based Animatable 3D Avatars with Morphable Model Alignment
https://github.com/orangesodahub/scenecraft
공식 코드https://github.com/pals-ttic/sjc
공식 코드Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation
https://github.com/pkunliu/isotropic3d
공식 코드Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding
https://github.com/priv-creation/concept-centric-personalization
공식 코드Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation
https://github.com/priv-creation/in-domain-generation-diffusion
공식 코드Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation
https://github.com/psc0628/dm-osvp
공식 코드Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning
https://github.com/qihao067/direct3d
공식 코드DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data
https://github.com/qinghew/StableIdentity
공식 코드StableIdentity: Inserting Anybody into Anywhere at First Sight
https://github.com/qizekun/vpp
공식 코드VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation
https://github.com/robincourant/DIRECTOR
공식 코드E.T. the Exceptional Trajectories: Text-to-camera-trajectory generation with character awareness
https://github.com/ruihangao/tactiledreamfusion
공식 코드Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation
https://github.com/sail-sg/consistent3d
공식 코드https://github.com/sisidai/InterFusion
공식 코드InterFusion: Text-Driven Generation of 3D Human-Object Interaction
https://github.com/skyworkai/mvgamba
공식 코드MVGamba: Unify 3D Content Generation as State Space Sequence Modeling
https://github.com/snuvclab/chupa
공식 코드Chupa: Carving 3D Clothed Humans from Skinned Shape Priors using 2D Diffusion Probabilistic Models
https://github.com/sunzey/alphaclip
공식 코드https://github.com/tau-yihouxiang/X-Ray
공식 코드https://github.com/tencent/flashvdm
공식 코드https://github.com/threestudio-project/threestudio
공식 코드Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation
https://github.com/trapoom555/gradeadreamer
공식 코드GradeADreamer: Enhanced Text-to-3D Generation Using Gaussian Splatting and Multi-View Diffusion
https://github.com/tyhuang0428/dreamcontrol
공식 코드DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior
https://github.com/vast-ai-research/triposr
공식 코드https://github.com/wakals/gascol
공식 코드Apply Hierarchical-Chain-of-Generation to Complex Attributes Text-to-3D Generation
https://github.com/wu-cvgl/mvcontrol
공식 코드MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation
https://github.com/wyysf-98/SweetDreamer
공식 코드SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3D
https://github.com/wyysf-98/craftsman
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.