Papers with Code 아카이브
Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/AnnnnnieZhang/M3D
공식 코드https://github.com/BoyanJIANG/LoRD
공식 코드LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modeling
https://github.com/DeepCamera/HDR-chroma-compression
공식 코드https://github.com/DonghwanKIM0101/MHCDIFF
공식 코드Multi-hypotheses Conditioned Point Cloud Diffusion for 3D Human Reconstruction from Occluded Images
https://github.com/HZXu-526/Human-Avatar
공식 코드Efficient Neural Implicit Representation for 3D Human Reconstruction
https://github.com/HongwenZhang/DaNet-DensePose2SMPL
공식 코드https://github.com/HongwenZhang/PyMAF
공식 코드PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
https://github.com/IDEA-Research/OSX
공식 코드One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer
https://github.com/JanaldoChen/Anim-NeRF
공식 코드https://github.com/Jiawei-Yao0812/NDCScene
공식 코드NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space
https://github.com/JihyongOh/JSI-GAN
공식 코드https://github.com/KU-CVLAB/3DFuse
공식 코드Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation
https://github.com/LizhenWangT/NormalGAN
공식 코드NormalGAN: Learning Detailed 3D Human from a Single RGB-D Image
https://github.com/MoyGcc/vid2avatar
공식 코드Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition
https://github.com/NJUVISION/CBUnet
공식 코드Rendering Nighttime Image Via Cascaded Color and Brightness Compensation
https://github.com/NikolaZubic/2dimageto3dmodel
공식 코드An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering
https://github.com/QianliM/CAPE
공식 코드https://github.com/ShichenLiu/SoftRas
공식 코드Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning
https://github.com/ShivamDuggal4/TARS3D
공식 코드Topologically-Aware Deformation Fields for Single-View 3D Reconstruction
https://github.com/SiTH-Diffusion/SiTH
공식 코드SiTH: Single-view Textured Human Reconstruction with Image-Conditioned Diffusion
https://github.com/Skype-line/X-Avatar
공식 코드https://github.com/SkyworkAI/Gamba
공식 코드Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction
https://github.com/Unity-Technologies/AnthroNet
공식 코드AnthroNet: Conditional Generation of Humans via Anthropometrics
https://github.com/Unity-Technologies/com.unity.cv.synthetichumans
공식 코드AnthroNet: Conditional Generation of Humans via Anthropometrics
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/Xharlie/DISN
공식 코드DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
https://github.com/Xharlie/ShapenetRender_more_variation
공식 코드DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
https://github.com/YadiraF/PIXIE
공식 코드Collaborative Regression of Expressive Bodies using Moderation
https://github.com/YueJiang-nj/CVPR2020-SDFDiff
공식 코드SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
https://github.com/YuliangXiu/ECON
공식 코드ECON: Explicit Clothed humans Optimized via Normal integration
https://github.com/ZhengZerong/DeepHuman
공식 코드https://github.com/ZhengZerong/PaMIR
공식 코드PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction
https://github.com/aamir-mustafa/distil_style
공식 코드Distilling Style from Image Pairs for Global Forward and Inverse Tone Mapping
https://github.com/aigc3d/LHM
공식 코드LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.