TrackEverything: Long Horizon Dense Tracking via De-Duplicating 3D Scene Representations

arXiv:2609.30222 · cs.CV, cs.AI, cs.RO · Submitted 2026-09-24 · Read on arXiv

cs.CV, cs.AI, cs.RO

Submitted: 2026-09-24

Updated: 2026-09-24

Project page: https://trackeverything.github.io

Terminology

• Accuracy — 18× in this paper · explained in 1 episode
• Transformer — 11× in this paper · explained in 2 episodes
• Decode — 10× in this paper · explained in 2 episodes
• Iterative Refinement — 9× in this paper · explained in 3 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Adam — 5× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 4× in this paper
• Generative AI — 4× in this paper · explained in 2 episodes
• Redundancy — 4× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Fidelity — 3× in this paper · explained in 2 episodes
• Aria Digital Twin — 2× in this paper
• Average Jaccard — 2× in this paper
• Digital Twin — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Occlusion Accuracy — 2× in this paper
• Optical Flow — 2× in this paper · explained in 2 episodes
• Point Clouds — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Annotation Errors — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
• DINOv2: Learning Robust Visual Features without Supervision
• Learning 3D Persistent Embodied World Models

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