Improving 3D Human Pose and Shape Estimation with Photorealistic Synthetic Data: The Generative BEDLAM Dataset
By carefully controlling the realism of synthetically generated images of humans, researchers can improve the accuracy of 3D human pose and shape estimation models while minimizing deviations from ground truth data.
원근법을 활용한 사람 정렬: CameraHMR
단일 이미지에서 정확한 3D 인간 포즈 및 형상 추정을 위해 카메라 내부 파라미터를 예측하는 HumanFoV 모델과 밀집 표면 키포인트 감지기를 활용한 CameraHMR 모델을 제시합니다.
CameraHMR: Enhancing 3D Human Pose and Shape Estimation Accuracy by Incorporating Camera Perspective
This research introduces CameraHMR, a novel method that significantly improves the accuracy of 3D human pose and shape estimation from monocular images by incorporating accurate camera perspective into both the training data generation and the model architecture.
단일 RGB 이미지에서 깊이 정보를 중간 표현으로 활용한 3D 인간 자세 및 형상 추정 방법: D-PoSE
D-PoSE는 단일 RGB 이미지에서 깊이 정보를 중간 표현으로 활용하여 3D 인간 자세 및 형상을 효과적으로 추정하는 가볍고 효율적인 방법이다.
単一RGB画像を用いた3D人体姿勢・形状推定のための新規アーキテクチャ：D-PoSE
D-PoSEは、深度情報を中間表現として活用することで、単一RGB画像から高精度な3D人体姿勢・形状推定を実現する、軽量かつ効率的なアーキテクチャである。
D-PoSE: Estimating 3D Human Pose and Shape from a Single RGB Image Using Depth as an Intermediate Representation
D-PoSE is a novel, lightweight method for estimating 3D human pose and shape from a single RGB image, achieving state-of-the-art accuracy by leveraging depth information learned from synthetic datasets as an intermediate representation.
Scaling Up 3D Human Recovery with Synthetic Game-Playing Data
Synthetic game-playing data from the GTA-V game engine can significantly improve the performance of 3D human recovery models, outperforming more sophisticated methods trained on real data alone.
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