Archived
2026-09-27TrackEverything: Long Horizon Dense Tracking via De-Duplicating 3D Scene Representations
Ayush Jain, Sreeharsha Paruchuri, Ishita Gupta, Fan Zhang, Tanner Schmidt, Jakob Engel, et al.
arXiv:2609.30222v1If you’ve ever wanted a tracker that can follow not just a few points, but essentially everything visible in a video for a very long time, this paper tackles that problem. Most tracking systems face a tradeoff: they either track sparse points over long sequences, or dense points only over short clips. TrackEverything breaks that limit by treating the video as a 3D scene that persists over time, rather than as a flat 2D stream. The key idea is to build tracks in world coordinates, merge duplicate observations as the camera moves, and then use a lightweight refinement stage to focus computation on points that actually move. That makes the method much more memory-efficient than traditional dense trackers. The result is impressive: it can track all visible points across videos longer than 1,000 frames on a single 40 GB GPU. This matters for robotics, AR, and scene understanding, where long-horizon, dense motion tracking is a basic building block.
Also spotted that day
LLM Agents Can Easily Tamper With Their Own TracesAD-WM: Action-Discriminative World Models for Counterfactual Model Predictive ControlTemporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement LearningAgentic Detection of Online ConspiraciesRAPID: Robot Agentic Programming from DemonstrationsRolling-WAM: World Action Models with Rolling ImaginationJevOut: Natural Context Can Flip Decision ModelsSemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete DataCoding Agents for Generalized Task and Motion Planning ProblemsTo Trust or Not to Trust: Retrieval-Augmented Fact Checking in SpeechPoEM: Predicting RL Outcomes from Existing PoliciesRequirement-Bound Verified Commissioning: A Frozen Four-Billion-Parameter Local Model as a Candidate Generator under an External Acceptance Layer with Verification and Release AuthorityMinimally Invasive Steering of Language ModelsInstrumental Monitor Evasion Emerges Under Ordinary Task Pressure
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