SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix
Video generation models have demonstrated great capabilities of producing
impressive monocular videos, however, the generation of 3D stereoscopic video
remains under-explored. We propose a pose-free and training-free approach for
generating 3D stereoscopic videos using an off-the-shelf monocular video
generation model. Our method warps a generated monocular video into camera
views on stereoscopic baseline using estimated video depth, and employs a novel
frame matrix video inpainting framework. The framework leverages the video
generation model to inpaint frames observed from different timestamps and
views. This effective approach generates consistent and semantically coherent
stereoscopic videos without scene optimization or model fine-tuning. Moreover,
we develop a disocclusion boundary re-injection scheme that further improves
the quality of video inpainting by alleviating the negative effects propagated
from disoccluded areas in the latent space. We validate the efficacy of our
proposed method by conducting experiments on videos from various generative
models, including Sora [4 ], Lumiere [2], WALT [8 ], and Zeroscope [ 42]. The
experiments demonstrate that our method has a significant improvement over
previous methods. The code will be released at
https://daipengwa.github.io/SVG_ProjectPage.
66837a5b2d89090894bf8fee
Peng Dai
66837a5b2d89090894bf8fef
Feitong Tan
66837a5b2d89090894bf8ff0
Qiangeng Xu
admin_assigned
2024-07-03T15:16:23.602Z
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qiangeng xu
user
xharlie
66837a5b2d89090894bf8ff1
David Futschik
66837a5b2d89090894bf8ff2
Ruofei Du
66837a5b2d89090894bf8ff3
Sean Fanello
66837a5b2d89090894bf8ff4
Xiaojuan Qi
claimed_verified
2025-07-15T19:14:44.635Z
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xiaojuan qi
user
xjqi
66837a5b2d89090894bf8ff5
Yinda Zhang
SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix
Video generation models have demonstrated great capabilities of producing
impressive monocular videos, however, the generation of 3D stereoscopic video
remains under-explored. We propose a pose-free and training-free approach for
generating 3D stereoscopic videos using an off-the-shelf monocular video
generation model. Our method warps a generated monocular video into camera
views on stereoscopic baseline using estimated video depth, and employs a novel
frame matrix video inpainting framework. The framework leverages the video
generation model to inpaint frames observed from different timestamps and
views. This effective approach generates consistent and semantically coherent
stereoscopic videos without scene optimization or model fine-tuning. Moreover,
we develop a disocclusion boundary re-injection scheme that further improves
the quality of video inpainting by alleviating the negative effects propagated
from disoccluded areas in the latent space. We validate the efficacy of our
proposed method by conducting experiments on videos from various generative
models, including Sora [4 ], Lumiere [2], WALT [8 ], and Zeroscope [ 42]. The
experiments demonstrate that our method has a significant improvement over
previous methods. The code will be released at
https://daipengwa.github.io/SVG_ProjectPage.
A pose-free, training-free method uses monocular video generation to produce high-quality 3D stereoscopic videos through warping, frame matrix inpainting, and disocclusion handling.
pose-free training-free monocular video generation stereoscopic videos video depth frame matrix video inpainting scene optimization model fine-tuning disocclusion boundary re-injection Sora Lumiere WALT Zeroscope