Abstract
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.
Motivation
Existing motion prediction methods struggle with short-term, sparse predictions and often fail to deliver accurate 3D motion estimations while optimization-based approaches require substantial time to process a single video.
We are the first method capable of efficiently tracking every pixel in 3D space over hundreds of frames from monocular videos, and achieves
state-of-the-art accuracy on 3D tracking benchmarks.
Method
DELTA takes RGB-D videos as input and achieves efficient dense
3D tracking using a coarse-to-fine strategy, beginning with coarse tracking through a spatio-temporal
attention mechanism at reduced resolution, followed by an attention-based upsampler
for high-resolution predictions.
Application: Non-rigid structure from motion
We first densely track pixels across multiple keyframes in the video to obtain pairwise correspondences.
Using these correspondences, we jointly estimate per-keyframe depth maps and camera poses through the Global Alignment in
DUSt3R and
MonST3R.
More results of video pose estimation can be found here.