Go-with-the-Track: Video Compositing and Motion Control with Point Tracking

Koichi Namekata; Yash Kant; Zhizheng Liu; Ryan D Burgert; Yuancheng Xu; Kuan Heng Lin; Emmett Steven; Julien Philip; Li Ma; Andrea Vedaldi; Paul Debevec; Ning Yu

arXiv · 2606.20891

Go-with-the-Track: Video Compositing and Motion Control with Point Tracking

Koichi Namekata·Yash Kant·Zhizheng Liu·Ryan D Burgert·Yuancheng Xu·Kuan Heng Lin·Emmett Steven·Julien Philip·Li Ma·Andrea Vedaldi·Paul Debevec·Ning Yu

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Abstract

Filmmaking demands precise motion control and reference image compositing -- capabilities that existing methods treat separately. Point-track-conditioned image-to-video models restrict content insertion to the first frame, while reference-to-video models lack fine-grained spatial-temporal control over how reference content integrates across frames. We present Go-with-the-Track, which unifies both capabilities by jointly conditioning on multiple reference images and reference-anchored point-tracks -- extending conventional point-tracks to explicitly establish correspondences between generated frames and reference images, thus enabling precise compositing and motion control throughout the video. To achieve this, we introduce spatially-aware point-track embeddings that encode the full sequence of point-track coordinates using a coordinate-wise MLP followed by temporal pooling. This representation captures the spatial characteristics of each point-track (serving as a unique identifier), while the embedding similarity correlates directly with spatial proximity, enhancing the model's ability to distinguish and associate point-tracks. We inject these point-track embeddings into a video diffusion transformer via a lightweight adapter, resolving the pixel-to-patch resolution mismatch while avoiding the substantial motion detail loss inherent in naive point-track subsampling. We use a hybrid training strategy to train jointly on dynamic, static, and synthetic scene video datasets to boost motion controllability. Experiments demonstrate that Go-with-the-Track achieves superior motion and reference control in a single model and enables new capabilities: multi-reference conditioned video generation with point-track driven compositing, as well as camera control for both static and dynamic scenes. Project Page: https://eyeline-labs.github.io/Go-with-the-Track/

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Koichi Namekata, Yash Kant, Zhizheng Liu, Ryan D Burgert, Yuancheng Xu, Kuan Heng Lin, Emmett Steven, Julien Philip, Li Ma, Andrea Vedaldi, Paul Debevec, Ning Yu. 2026-06-18. Go-with-the-Track: Video Compositing and Motion Control with Point Tracking. https://arxiv.org/abs/2606.20891

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