SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix
Authors: name: Peng Dai, name: Feitong Tan, name: Qiangeng Xu, name: David Futschik, name: Ruofei Du, name: Sean Fanello, name: Xiaojuan Qi, name: Yinda Zhang

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.