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
Papers with Code 아카이브
3D Human Pose Estimation
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3D Human Pose Estimation is a computer vision task that involves estimating the 3D positions and orientations of body joints and bones from 2D images or videos. The goal is to reconstruct the 3D pose of a person in real-time, which can be used in a variety of applications, such as virtual reality, human-computer interaction, and motion analysis.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PKU-ICST-MIPL/FinePOSE_CVPR2024
공식 코드FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via Diffusion Models
https://github.com/PaddlePaddle/PaddleGAN
공식 코드https://github.com/PantoMatrix/PantoMatrix
공식 코드https://github.com/Project-Splinter/MonoPort
공식 코드https://github.com/QianliM/CAPE
공식 코드https://github.com/SadilKhan/MARVEL-FX3D
공식 코드MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
https://github.com/Santa-Clara-Media-Lab/SoloPose
공식 코드SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data Augmentation
https://github.com/ShirleyMaxx/VirtualMarker
공식 코드https://github.com/Snapchat/MeshPose
공식 코드MeshPose: Unifying DensePose and 3D Body Mesh reconstruction
https://github.com/TCXX/ObjaversePlusPlus
공식 코드Objaverse++: Curated 3D Object Dataset with Quality Annotations
https://github.com/Tang1705/Semantic-Lens-AAAI24
공식 코드Semantic Lens: Instance-Centric Semantic Alignment for Video Super-Resolution
https://github.com/TencentARC/GFPGAN
공식 코드Towards Real-World Blind Face Restoration with Generative Facial Prior
https://github.com/Thmen/EGVSR
공식 코드Real-Time Super-Resolution System of 4K-Video Based on Deep Learning
https://github.com/TimoBolkart/voca
공식 코드https://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/Vchitect/LaVie
공식 코드LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
https://github.com/Vegetebird/MHFormer
공식 코드MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation
https://github.com/Vegetebird/StridedTransformer-Pose3D
공식 코드Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation
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/Walter0807/MotionBERT
공식 코드MotionBERT: A Unified Perspective on Learning Human Motion Representations
https://github.com/Westlake-AI/MogaNet
공식 코드https://github.com/Westlake-AI/openmixup
공식 코드https://github.com/XinyiYing/D3Dnet
공식 코드https://github.com/YadiraF/DECA
공식 코드Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
https://github.com/YadiraF/PIXIE
공식 코드Collaborative Regression of Expressive Bodies using Moderation
https://github.com/Ysz2022/Fourier123
공식 코드Hybrid Fourier Score Distillation for Efficient One Image to 3D Object Generation
https://github.com/YxZhxn/Ray3D
공식 코드Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization
https://github.com/ZhengyiLuo/MEVA
공식 코드3D Human Motion Estimation via Motion Compression and Refinement
https://github.com/Zhongdao/UniTrack
공식 코드Do Different Tracking Tasks Require Different Appearance Models?
https://github.com/adjkamel/goa_bat
공식 코드3D Human Pose Estimation via Spatial Graph Order Attention and Temporal Body Aware Transformer
https://github.com/ailingzengzzz/Split-and-Recombine-Net
공식 코드SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach
https://github.com/akanazawa/human_dynamics
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.