3D Affordance Reconstruction from Egocentric Demonstration Video

Egil Diau, Yueh-Feng Ku, Chi Ming Chung, Ting-Jun Wang, Min Sun, Winston H. Hsu

Keywords: 3d Affordance, Egocentric Vision, Learning from Demonstration

Abstract:

Developing robots capable of generalized skills remains an exceedingly challenging task. Drawing from psychology, the concept of affordance has emerged as a promising intermediate representation to guide robot manipulation. However, prior work has primarily focused on 2D affordances from video, neglecting critical spatial information such as camera positioning, absolute position, depth and geometry. In this paper, we present a novel training-free method that constructs 3D affordances from egocentric demonstration videos. To address the challenge of insufficient static, high-quality frames for 3D reconstruction in egocentric videos, we employ the 3D foundational model DUST3R, which reconstructs scenes from sparse images without requiring COLMAP. We analyze videos using hand detection to identify contact times and 2D contact points, reconstruct these interactions using DUST3R, and project the 2D contact points into 3D space using gaussian heatmaps. Finally, we derive hand trajectories through 3D hand pose estimation and process them using linear regression to integrate the spatiotemporal dynamics of human-object interactions. We demonstrate the effectiveness of our method on the ego4d-exo dataset for seven real-world hand-object manipulation tasks in cooking scenes.

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 7202