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This is a Quantitative Study study (also classified as: Experimental Study).

October 9, 2025Open Access

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation

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FVGen reduces sampling time by over 90%, vastly improving efficiency in novel view synthesis.
The framework uses video diffusion model distillation, leveraging generative adversarial networks for denoising.
Extensive experiments demonstrate that FVGen maintains or exceeds visual quality compared to prior methods.
FVGen is particularly beneficial for 3D reconstruction tasks with sparse input views requiring multiple VDM runs.

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Framework accelerates novel view synthesis with video diffusion models in sparse views, indicating enhanced efficiency.

Teng et al. (2025) studied this question.

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