Jernej Barbic

Zhiyuan Gao, Jiageng Mao, Hong-Xing Yu et al.

A longstanding goal in computer vision is to model motions from videos, while the representations behind motions, i.e. the invisible physical interactions that cause objects to deform and move, remain largely unexplored. In this paper, we study how to recover the invisible forces from visual observations, e.g., estimating the wind field by observing a leaf falling to the ground. Our key innovation is an end-to-end differentiable inverse graphics framework, which jointly models object geometry, physical properties, and interactions directly from videos. Through backpropagation, our approach enables the recovery of force representations from object motions. We validate our method on both synthetic and real-world scenarios, and the results demonstrate its ability to infer plausible force fields from videos. Furthermore, we show the potential applications of our approach, including physics-based video generation and editing. We hope our approach sheds light on understanding and modeling the physical process behind pixels, bridging the gap between vision and physics. Please check more video results in our \href{https://chaoren2357.github.io/seeingthewind/}{project page}.

Jingtao Huang, Bohan Wang, Zhiyuan Gao et al.

We study the design of transfer functions for volumetric rendering of magnetic resonance imaging (MRI) datasets of human hands. Human hands are anatomically complex, containing various organs within a limited space, which presents challenges for volumetric rendering. We focus on hand musculoskeletal organs because they are volumetrically the largest inside the hand, and most important for the hand's main function, namely manipulation of objects. While volumetric rendering is a mature field, the choice of the transfer function for the different organs is arguably just as important as the choice of the specific volume rendering algorithm; we demonstrate that it significantly influences the clarity and interpretability of the resulting images. We assume that the hand MRI scans have already been segmented into the different organs (bones, muscles, tendons, ligaments, subcutaneous fat, etc.). Our method uses the hand MRI volume data, and the geometry of its inner organs and their known segmentation, to produce high-quality volume rendering images of the hand, and permits fine control over the appearance of each tissue. We contribute two families of transfer functions to emphasize different hand tissues of interest, while preserving the visual context of the hand. We also discuss and reduce artifacts present in standard volume ray-casting of human hands. We evaluate our volumetric rendering on five challenging hand motion sequences. Our experimental results demonstrate that our method improves hand anatomy visualization, compared to standard surface and volume rendering techniques.