Learning to Identify Physical Parameters from Video Using Differentiable Physics

Movie illustration discovering is staying utilised for scene prediction or vision-primarily based scheduling. To start

Movie illustration discovering is staying utilised for scene prediction or vision-primarily based scheduling. To start with, the picture is encoded in a latent scene illustration. Then, long run frames are predicted. Versions, primarily based on neural networks, discover this illustration devoid of interpreting bodily quantities, like mass, placement, or velocity. As a result, this sort of styles may have limited explainability and be tricky to generalize for new jobs and scenarios.

A recent research indicates an strategy for determining bodily parameters of objects from video. Illustrations or photos are encoded into bodily states and long run scenes are predicted with the assistance of a differentiable physics motor. These types of scenarios as Block Pushed On a Flat Plane, Block Colliding With One more Block, or Block Freefall and Sliding Down On an Inclined Plane ended up simulated. Satisfactory video prediction benefits ended up accomplished utilizing each supervised and self-supervised discovering.

Movie illustration discovering has a short while ago captivated interest in laptop or computer vision thanks to its applications for exercise and scene forecasting or vision-primarily based scheduling and control. Movie prediction styles often discover a latent illustration of video which is encoded from input frames and decoded back into images. Even when conditioned on actions, purely deep discovering primarily based architectures generally deficiency a bodily interpretable latent place. In this research, we use a differentiable physics motor within just an motion-conditional video illustration community to discover a bodily latent illustration. We suggest supervised and self-supervised discovering techniques to practice our community and identify bodily qualities. The latter works by using spatial transformers to decode bodily states back into images. The simulation scenarios in our experiments comprise pushing, sliding and colliding objects, for which we also evaluate the observability of the bodily qualities. In experiments we reveal that our community can discover to encode images and identify bodily qualities like mass and friction from video clips and motion sequences in the simulated scenarios. We consider the precision of our supervised and self-supervised techniques and compare it with a process identification baseline which right learns from condition trajectories. We also reveal the capability of our approach to predict long run video frames from input images and actions.

Connection: https://arxiv.org/stomach muscles/2009.08292