https://github.com/3dpose/GnTCN
공식 코드Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos
Papers with Code 아카이브
3D Absolute Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 3
- 보존된 평가 기록
- 6
This task aims to solve absolute (camera-centric not root-relative) 3D human pose estimation.
<span style="color:grey; opacity: 0.6">( Image credit: RootNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Adamdad/hash3D
공식 코드https://github.com/AiuniAI/Unique3D
공식 코드Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
https://github.com/AlanZhang1995/TwoStreamSR
공식 코드Two-Stream Action Recognition-Oriented Video Super-Resolution
https://github.com/Ascend-Research/Turtle
공식 코드Learning Truncated Causal History Model for Video Restoration
https://github.com/CHUNYUWANG/imu-human-pose-pytorch
공식 코드Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach
https://github.com/CS-GangXu/TMNet
공식 코드Temporal Modulation Network for Controllable Space-Time Video Super-Resolution
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/Dianezzy/ParaLip
공식 코드https://github.com/Doubiiu/CodeTalker
공식 코드CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior
https://github.com/EnVision-Research/Kiss3DGen
공식 코드Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation
https://github.com/EscVM/RAMS
공식 코드https://github.com/EvelynFan/FaceFormer
공식 코드FaceFormer: Speech-Driven 3D Facial Animation with Transformers
https://github.com/Healthcare-Robotics/bodies-at-rest
공식 코드Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
https://github.com/HendrikMuenster/superResolution
공식 코드https://github.com/JaeYeonKang/STVUN-Pytorch
공식 코드https://github.com/JihyongOh/FISR
공식 코드FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal Loss
https://github.com/Lakonik/MVEdit
공식 코드Generic 3D Diffusion Adapter Using Controlled Multi-View Editing
https://github.com/LongguangWang/SOF-VSR
공식 코드Deep Video Super-Resolution using HR Optical Flow Estimation
https://github.com/LongguangWang/SOF-VSR-Super-Resolving-Optical-Flow-for-Video-Super-Resolution-
공식 코드Learning for Video Super-Resolution through HR Optical Flow Estimation
https://github.com/MoyGcc/hpcwild
공식 코드https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020
공식 코드Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
https://github.com/Nithin-GK/UniteandConquer
공식 코드Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
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/PaddlePaddle/PaddleGAN
공식 코드https://github.com/PantoMatrix/PantoMatrix
공식 코드https://github.com/Project-Splinter/MonoPort
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/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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.