ViDAR: Video Diffusion-Aware 4D Reconstruction From Monocular Inputs
Authors: name: Michal Nazarczuk, name: Sibi Catley-Chandar, name: Thomas Tanay, name: Zhensong Zhang, name: Gregory Slabaugh, name: Eduardo Pérez-Pellitero

Dynamic Novel View Synthesis aims to generate photorealistic views of moving
subjects from arbitrary viewpoints. This task is particularly challenging when
relying on monocular video, where disentangling structure from motion is
ill-posed and supervision is scarce. We introduce Video Diffusion-Aware
Reconstruction (ViDAR), a novel 4D reconstruction framework that leverages
personalised diffusion models to synthesise a pseudo multi-view supervision
signal for training a Gaussian splatting representation. By conditioning on
scene-specific features, ViDAR recovers fine-grained appearance details while
mitigating artefacts introduced by monocular ambiguity. To address the
spatio-temporal inconsistency of diffusion-based supervision, we propose a
diffusion-aware loss function and a camera pose optimisation strategy that
aligns synthetic views with the underlying scene geometry. Experiments on
DyCheck, a challenging benchmark with extreme viewpoint variation, show that
ViDAR outperforms all state-of-the-art baselines in visual quality and
geometric consistency. We further highlight ViDAR's strong improvement over
baselines on dynamic regions and provide a new benchmark to compare performance
in reconstructing motion-rich parts of the scene. Project page:
https://vidar-4d.github.io