Stable Pose
Stable pose estimation focuses on accurately determining the three-dimensional orientation and position of objects or body parts, particularly in challenging scenarios like occlusion or complex movements. Current research emphasizes robust methods using various model architectures, including transformers, diffusion models, and neural radiance fields, often incorporating multi-modal data (e.g., RGB images, depth, audio, text) to improve accuracy and generalization. This field is crucial for advancements in robotics, augmented and virtual reality, human-computer interaction, and other applications requiring precise understanding of object or human pose in dynamic environments.
Papers
Learning a Category-level Object Pose Estimator without Pose Annotations
Fengrui Tian, Yaoyao Liu, Adam Kortylewski, Yueqi Duan, Shaoyi Du, Alan Yuille, Angtian Wang
TIM: A Time Interval Machine for Audio-Visual Action Recognition
Jacob Chalk, Jaesung Huh, Evangelos Kazakos, Andrew Zisserman, Dima Damen