IPAB Workshop-04/04/2024 Title: Few-shot self-supervised articulated object understanding and rendering Abstract: Articulated object understanding, the study of objects with independently moving parts, is critical in fields like robotics, virtual/augmented reality, and animation. Despite its importance, conventional methods, primarily reliant on labeled 3D data or numerous images, face difficulties due to costly and time-consuming data collection. This work presents a unique approach, employing few-shot, self-supervised learning to separately estimate parts and motion parameters, optimizing them in an iterative manner—significantly reducing reliance on large amounts of training data. Our approach utilizes a pretrained renderer such as NeRF and a limited set of images depicting the object in a different articulation with noted camera positions. Our method enables the efficient estimation of motion parameters. It also allows us to use part-aware composite rendering, where the parts of the object are effectively repositioned to represent different movements. As a result, we can render images that accurately depict different articulations. Notably, our method demonstrates remarkable accuracy in motion parameter estimation, competing with or even exceeding previous methods while utilizing considerably fewer images. Apr 04 2024 13.00 - 14.00 IPAB Workshop-04/04/2024 Jianning Deng G.03
IPAB Workshop-04/04/2024 Title: Few-shot self-supervised articulated object understanding and rendering Abstract: Articulated object understanding, the study of objects with independently moving parts, is critical in fields like robotics, virtual/augmented reality, and animation. Despite its importance, conventional methods, primarily reliant on labeled 3D data or numerous images, face difficulties due to costly and time-consuming data collection. This work presents a unique approach, employing few-shot, self-supervised learning to separately estimate parts and motion parameters, optimizing them in an iterative manner—significantly reducing reliance on large amounts of training data. Our approach utilizes a pretrained renderer such as NeRF and a limited set of images depicting the object in a different articulation with noted camera positions. Our method enables the efficient estimation of motion parameters. It also allows us to use part-aware composite rendering, where the parts of the object are effectively repositioned to represent different movements. As a result, we can render images that accurately depict different articulations. Notably, our method demonstrates remarkable accuracy in motion parameter estimation, competing with or even exceeding previous methods while utilizing considerably fewer images. Apr 04 2024 13.00 - 14.00 IPAB Workshop-04/04/2024 Jianning Deng G.03
