https://github.com/yfeng95/deca
Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
POTTER: Pooling Attention Transformer for Efficient Human Mesh Recovery
Recurrent Back-Projection Network for Video Super-Resolution
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wild
A simple yet effective baseline for 3d human pose estimation
Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views
Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views
TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style
RapidPoseTriangulation: Multi-view Multi-person Whole-body Human Pose Triangulation in a Millisecond
MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.