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Image to 3D
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AiuniAI/Unique3D
공식 코드Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
https://github.com/Chenfeng1271/L3A
공식 코드https://github.com/Costwen/Ouroboros3D
공식 코드Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion
https://github.com/EnVision-Research/Kiss3DGen
공식 코드Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation
https://github.com/Lakonik/MVEdit
공식 코드Generic 3D Diffusion Adapter Using Controlled Multi-View Editing
https://github.com/One-2-3-45/One-2-3-45
공식 코드One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization
https://github.com/SadilKhan/MARVEL-FX3D
공식 코드MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
https://github.com/TCXX/ObjaversePlusPlus
공식 코드Objaverse++: Curated 3D Object Dataset with Quality Annotations
https://github.com/VITA-Group/NeuralLift-360
공식 코드NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360° Views
https://github.com/VincentHancoder/REPARO
공식 코드REPARO: Compositional 3D Assets Generation with Differentiable 3D Layout Alignment
https://github.com/WU-CVGL/MVControl-threestudio
공식 코드Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting
https://github.com/Ysz2022/Fourier123
공식 코드Hybrid Fourier Score Distillation for Efficient One Image to 3D Object Generation
https://github.com/byeongjun-park/HarmonyView
공식 코드HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D
https://github.com/continental/6img-to-3d
공식 코드6Img-to-3D: Few-Image Large-Scale Outdoor Driving Scene Reconstruction
https://github.com/cvlab-columbia/zero123
공식 코드https://github.com/donydchen/mvsplat
공식 코드MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images
https://github.com/ephemeral182/empirical-study-of-gpt-4o-image-gen
공식 코드https://github.com/fudan-zvg/efficient4d
공식 코드Efficient4D: Fast Dynamic 3D Object Generation from a Single-view Video
https://github.com/guochengqian/magic123
공식 코드Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors
https://github.com/ipl-uw/ZeDO-Release
공식 코드Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation
https://github.com/johannwyh/meat
공식 코드MEAT: Multiview Diffusion Model for Human Generation on Megapixels with Mesh Attention
https://github.com/junwuzhang19/repaint123
공식 코드https://github.com/justimyhxu/grm
공식 코드GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation
https://github.com/lin-jinwei/OneTo3D
공식 코드OneTo3D: One Image to Re-editable Dynamic 3D Model and Video Generation
https://github.com/liuyuan-pal/syncdreamer
공식 코드SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
https://github.com/lutao2021/DiMeR
공식 코드https://github.com/pku-yuangroup/envision3d
공식 코드https://github.com/pkunliu/isotropic3d
공식 코드Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding
https://github.com/ruihangao/tactiledreamfusion
공식 코드Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation
https://github.com/ssfootball04/generative_pose
공식 코드Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking
https://github.com/suyz526/zebrapose
공식 코드ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
https://github.com/tencentarc/instantmesh
공식 코드https://github.com/tingtingliao/soap
공식 코드https://github.com/transpchan/Live3D
공식 코드https://github.com/yanghb22-fdu/hi3d-official
공식 코드Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.