title: DELTA: Dense Efficient Long-range 3D Tracking for any video
summary: Tracking dense 3D motion from monocular videos remains challenging,
  particularly when aiming for pixel-level precision over long sequences. We
  introduce \Approach, 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, \Approach 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 \Approach 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.
paper_authors._id: 67245ee3003a1dfcc5cc2577
paper_authors.name: Tuan Duc Ngo
paper_authors._id: 67245ee3003a1dfcc5cc2578
paper_authors.name: Peiye Zhuang
paper_authors._id: 67245ee3003a1dfcc5cc2579
paper_authors.name: Chuang Gan
paper_authors._id: 67245ee3003a1dfcc5cc257a
paper_authors.name: Evangelos Kalogerakis
paper_authors._id: 67245ee3003a1dfcc5cc257b
paper_authors.name: Sergey Tulyakov
paper_authors._id: 67245ee3003a1dfcc5cc257c
paper_authors.name: Hsin-Ying Lee
paper_authors._id: 67245ee3003a1dfcc5cc257d
paper_authors.name: Chaoyang Wang
paper_title: DELTA: Dense Efficient Long-range 3D Tracking for any video
paper_summary: Tracking dense 3D motion from monocular videos remains challenging,
  particularly when aiming for pixel-level precision over long sequences. We
  introduce \Approach, 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, \Approach 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 \Approach 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.
paper_ai_keywords: joint global-local attention mechanism transformer-based upsampler log-depth