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DELTA: Dense Efficient Long-range 3D Tracking for any video

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arxiv 2410.24211 v3 pith:EWNOIZ5R submitted 2024-10-31 cs.CV

Tuan Duc Ngo , Peiye Zhuang , Chuang Gan , Evangelos Kalogerakis , Sergey Tulyakov , Hsin-Ying Lee , Chaoyang Wang This is my paper
classification cs.CV
keywords trackingdeltadensemotionachievingmethodmethodsspace
verification ladder T0 review T1 audit T2 compute T3 formal
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Tracking dense 3D motion from monocular videos remains challenging, particularly when aiming for pixel-level precision over long sequences. We introduce DELTA, a novel method that efficiently tracks every pixel in 3D space, enabling accurate motion estimation across entire videos. Our approach leverages a joint global-local attention mechanism for reduced-resolution tracking, followed by a transformer-based upsampler to achieve high-resolution predictions. Unlike existing methods, which are limited by computational inefficiency or sparse tracking, DELTA delivers dense 3D tracking at scale, running over 8x faster than previous methods while achieving state-of-the-art accuracy. Furthermore, we explore the impact of depth representation on tracking performance and identify log-depth as the optimal choice. Extensive experiments demonstrate the superiority of DELTA on multiple benchmarks, achieving new state-of-the-art results in both 2D and 3D dense tracking tasks. Our method provides a robust solution for applications requiring fine-grained, long-term motion tracking in 3D space.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MegaFlow: Zero-Shot Large Displacement Optical Flow

    cs.CV 2026-03 accept novelty 6.0 of 10

    MegaFlow reaches SOTA zero-shot optical flow (especially large motions) and competitive point tracking by global matching of pre-trained ViT features followed by lightweight multi-frame refinement.

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  3. SpatialTrackerV2: 3D Point Tracking Made Easy

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A single feed-forward model jointly estimates video depth, camera poses, and 3D point trajectories from monocular video, setting a new state of the art on TAPVid-3D.

  4. AMPLIFY: Actionless Motion Priors for Robot Learning from Videos

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A three-stage pipeline that turns keypoint tracks into discrete motion tokens, predicts them from action-free video, and decodes them into actions yields large few-shot and zero-shot policy improvements in robot manipulation.

  5. Reconstructing 4D Spatial Intelligence: A Survey

    cs.CV 2025-07 accept novelty 4.0 of 10

    A review that classifies 4D scene reconstruction methods into five progressive levels: low-level cues, scene components, dynamic scenes, interactions, and physics.

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