Holo4D: Holistic 4D Reconstruction as Geometric Control for Video Diffusion
We introduce Holo4D, a video reshooting framework that uses reconstructed 3D trajectories as a holistic source-view 4D scaffold.
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Key points
- Synthesizing a monocular video from a new camera trajectory requires preserving the observed scene geometry and motion while completing regions that become newly visible.
- Feed-forward 4D reconstructors recover the structure of the observed scene, whereas video diffusion models provide a generative prior for completing unseen content.
- We project these trajectories into the requested views and encode their geometry and source appearance to guide a pretrained video diffusion model.
- Experiments demonstrate the value of trajectory-based conditioning for camera control and novel-view synthesis.
Sources (1)
- [1]Holo4D: Holistic 4D Reconstruction as Geometric Control for Video DiffusionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 01:47 PM
We introduce Holo4D, a video reshooting framework that uses reconstructed 3D trajectories as a holistic source-view 4D scaffold.
Synthesizing a monocular video from a new camera trajectory requires preserving the observed scene geometry and motion while completing regions that become newly visible.
Extractive summary: sentences quoted from the sources.
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