Abstract
Comment: Project Page : https://cvlab-kaist.github.io/MVTrack4Gen/
MVTrack4Gen: Multi-View Point Tracking as Geometric Supervision for 4D Video Generation
- Authors
- JoungBin Lee, Jaewoo Jung, Jongmin Lee...
- Categories
- cs.CV
Brief
MVTrack4Gen (Lee et al., arXiv 2026-06-24) augments camera-conditioned novel-view video diffusion models with multi-view point-tracking supervision. It routes attention-layer correspondence features into an auxiliary tracking head and jointly trains a point-tracking loss, improving motion fidelity and cross-view geometric consistency; authors report state-of-the-art geometric consistency and competitive camera accuracy across benchmarks.
Why it matters
MVTrack4Gen (Lee et al., 2026-06-24) augments camera-conditioned novel-view video diffusion models by routing attention-layer correspondence features into an auxiliary multi-view point-tracking head and jointly training a point-tracking loss, explicitly supervising geometry and motion to reduce cross-view and temporal misalignment.
Key details
- The authors report state-of-the-art geometric consistency and competitive camera accuracy across diverse benchmarks; project page: https://cvlab-kaist.github.io/MVTrack4Gen/, arXiv: https://arxiv.org/abs/2606.26087v1.