Dynamic View Synthesis as an Inverse Problem
In this work, we address dynamic view synthesis from monocular videos as an
inverse problem in a training-free setting. By redesigning the noise
initialization phase of a pre-trained video diffusion model, we enable
high-fidelity dynamic view synthesis without any weight updates or auxiliary
modules. We begin by identifying a fundamental obstacle to deterministic
inversion arising from zero-terminal signal-to-noise ratio (SNR) schedules and
resolve it by introducing a novel noise representation, termed K-order
Recursive Noise Representation. We derive a closed form expression for this
representation, enabling precise and efficient alignment between the
VAE-encoded and the DDIM inverted latents. To synthesize newly visible regions
resulting from camera motion, we introduce Stochastic Latent Modulation, which
performs visibility aware sampling over the latent space to complete occluded
regions. Comprehensive experiments demonstrate that dynamic view synthesis can
be effectively performed through structured latent manipulation in the noise
initialization phase.
68488c1c42e4f9106973f1d9
Hidir Yesiltepe
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2025-06-11T08:35:23.685Z
63597e84d72fc0539e72b507
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Hidir Yesiltepe
user
Hidir
68488c1c42e4f9106973f1da
Pinar Yanardag
extracted_pending
2025-06-10T19:48:45.250Z
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Pinar Yanardag
user
Pinguar
Dynamic View Synthesis as an Inverse Problem
In this work, we address dynamic view synthesis from monocular videos as an
inverse problem in a training-free setting. By redesigning the noise
initialization phase of a pre-trained video diffusion model, we enable
high-fidelity dynamic view synthesis without any weight updates or auxiliary
modules. We begin by identifying a fundamental obstacle to deterministic
inversion arising from zero-terminal signal-to-noise ratio (SNR) schedules and
resolve it by introducing a novel noise representation, termed K-order
Recursive Noise Representation. We derive a closed form expression for this
representation, enabling precise and efficient alignment between the
VAE-encoded and the DDIM inverted latents. To synthesize newly visible regions
resulting from camera motion, we introduce Stochastic Latent Modulation, which
performs visibility aware sampling over the latent space to complete occluded
regions. Comprehensive experiments demonstrate that dynamic view synthesis can
be effectively performed through structured latent manipulation in the noise
initialization phase.
Dynamic view synthesis from monocular videos is achieved through noise initialization redesign of a pre-trained diffusion model using K-order Recursive Noise Representation and Stochastic Latent Modulation.
dynamic view synthesis monocular videos inverse problem training-free setting noise initialization pre-trained video diffusion model zero-terminal signal-to-noise ratio K-order Recursive Noise Representation VAE-encoded DDIM inverted latents Stochastic Latent Modulation visibility aware sampling