Featurising Pixels from Dynamic 3D Scenes with Linear In-Context Learners

One of the most exciting applications of vision models involve pixel-level reasoning.Despite the abundance of vision foundation models, we still lack representations that effectively embed spatio-temporal properties of visual scenes at the pixel level.Existing frameworks either train on image-based pretext tasks, which do not account for dynamic elements, or on video sequences for action-level reasoning, which does not scale to dense pixel-level prediction.We present a framework that learns pixel-accurate feature descriptors from videos, LILA.The core element of our training framework is linear in-context learning.LILA leverages spatio-temporal cue maps -- depth and motion -- estimated with off-the-shelf networks.Despite the noisy nature of those cues, LILA trains effectively on uncurated video datasets, embedding semantic and geometric properties in a temporally consistent manner.We demonstrate compelling empirical benefits of the learned representation across a diverse suite of vision tasks: video object segmentation, surface normal estimation and semantic segmentation.