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NVS-Solver: Video Diffusion Model as Zero-Shot Novel View Synthesizer
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NVS-Solver: Video Diffusion Model as Zero-Shot Novel View Synthesizer
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By harnessing the potent generative capabilities of pre-trained large video diffusion models, we propose NVS-Solver, a new novel view synthesis (NVS) paradigm that operates \textit{without} the need for training. NVS-Solver adaptively modulates the diffusion sampling process with the given views to enable the creation of remarkable visual experiences from single or multiple views of static scenes or monocular videos of dynamic scenes. Specifically, built upon our theoretical modeling, we iteratively modulate the score function with the given scene priors represented with warped input views to control the video diffusion process. Moreover, by theoretically exploring the boundary of the estimation error, we achieve the modulation in an adaptive fashion according to the view pose and the number of diffusion steps. Extensive evaluations on both static and dynamic scenes substantiate the significant superiority of our NVS-Solver over state-of-the-art methods both quantitatively and qualitatively. \textit{ Source code in } \href{https://github.com/ZHU-Zhiyu/NVS_Solver}{https://github.com/ZHU-Zhiyu/NVS$\_$Solver}.
Forward citations
Cited by 12 Pith papers
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MoCam: Unified Novel View Synthesis via Structured Denoising Dynamics
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UniGeo unifies geometric guidance across three levels in video models to reduce geometric drift and improve consistency in camera-controllable image editing.
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OrthoMotion disentangles camera and subject motion in video generation by splitting attention into algebraically complementary geometric (RoPE rotation) and semantic (gated value) channels driven to orthogonality by a...
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ParaScale: Scale-Calibrated Camera-Motion Transfer via a Gauge-Invariant Parallax Number
ParaScale extracts a gauge-invariant Parallax Number from a reference video and re-realizes the same parallax against the target scene's depth map to achieve scale-calibrated camera motion transfer.
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Warp-as-History: Generalizable Camera-Controlled Video Generation from One Training Video
Warp-as-History enables zero-shot camera trajectory following in frozen video models by supplying camera-warped pseudo-history, with single-video LoRA fine-tuning improving generalization to unseen videos.
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UniGeo: Unifying Geometric Guidance for Camera-Controllable Image Editing via Video Models
UniGeo adds unified geometric guidance at three levels in video models to reduce geometric drift and improve structural fidelity in camera-controllable image editing.
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Real2SAM2Real: Generative 3D Caches as Complementary Context for Video Diffusion
Real2SAM2Real uses 3D caches from lifting models as complementary context for video diffusion models to enable precise decoupled control over camera trajectories and multi-entity motions while maintaining spatiotempor...
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Can These Views Be One Scene? Evaluating Multiview 3D Consistency when 3D Foundation Models Hallucinate
Introduces a robustness benchmark for multiview 3D consistency and COLMAP-based metrics that better detect hallucinations in 3D foundation models than existing neural metrics.
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UniGeo: Unifying Geometric Guidance for Camera-Controllable Image Editing via Video Models
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