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arXiv · 2509.06579

CausNVS: Autoregressive Multi-view Diffusion for Flexible 3D Novel View Synthesis

Xin Kong·Daniel Watson·Yannick Strümpler·Michael Niemeyer·Federico Tombari
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Abstract

Multi-view diffusion models have shown promise in 3D novel view synthesis, but most existing methods adopt a non-autoregressive formulation. This limits their applicability in world modeling, as they only support a fixed number of views and suffer from slow inference due to denoising all frames simultaneously. To address these limitations, we propose CausNVS, a multi-view diffusion model in an autoregressive setting, which supports arbitrary input-output view configurations and generates views sequentially. We train CausNVS with causal masking and per-frame noise, using pairwise-relative camera pose encodings (CaPE) for precise camera control. At inference time, we combine a spatially-aware sliding-window with key-value caching and noise conditioning augmentation to mitigate drift. Our experiments demonstrate that CausNVS supports a broad range of camera trajectories, enables flexible autoregressive novel view synthesis, and achieves consistently strong visual quality across diverse settings. Project page: https://kxhit.github.io/CausNVS.html.

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Xin Kong, Daniel Watson, Yannick Strümpler, Michael Niemeyer, Federico Tombari. 2025-09-08. CausNVS: Autoregressive Multi-view Diffusion for Flexible 3D Novel View Synthesis. https://arxiv.org/abs/2509.06579

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