ViDAR: Video Diffusion-Aware 4D Reconstruction From Monocular Inputs
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
685a72a00e4ad7e219758702
Michal Nazarczuk
claimed_verified
2025-06-24T12:43:11.760Z
66f44a3df9252e0f50b59fdb
/avatars/4234203c77a0e6f594f3de26bfe8c649.svg
Michal Nazarczuk
user
michaal94
685a72a00e4ad7e219758703
Sibi Catley-Chandar
685a72a00e4ad7e219758704
Thomas Tanay
685a72a00e4ad7e219758705
Zhensong Zhang
685a72a00e4ad7e219758706
Gregory Slabaugh
685a72a00e4ad7e219758707
Eduardo Pérez-Pellitero
ViDAR: Video Diffusion-Aware 4D Reconstruction From Monocular Inputs
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
ViDAR uses diffusion-aware reconstruction to generate high-quality novel views of dynamic scenes from monocular video, outperforming existing methods in visual quality and geometric consistency.
Video Diffusion-Aware Reconstruction ViDAR Gaussian splatting diffusion models spatio-temporal inconsistency diffusion-aware loss function camera pose optimisation DyCheck benchmark