Original title: Stabilizing Camera-Controlled Novel View Synthesis at Inference Time
Prajwal Singh, Arjun Badola, Seema Kumari, Hajime Nagahara +1 · September 2026
Computer models can create videos of a scene from new angles using just one starting image. But when the camera moves too far or creates a long video, the pictures often warp and break down. Researchers tested why this happens. They found a simple fix: break large camera moves into tiny, one-at-a-time steps. Taking small steps limits shape distortion and stops errors from piling up. In tests, the video stays stable during small movements. However, it gets much worse when a single step reaches 18 to 20 degrees. Adding extra tools to guide shapes and preserve colors helps even more. This shows that careful step-by-step camera control makes video creation stable without retraining the underlying model.
Imagine trying to draw a sequence of pictures where each drawing shows a slightly different view of the same object. If you try to jump to a very different angle in one drawing, it's hard to keep the proportions right. In this paper, the researchers have the system make small adjustments in each step instead, so each drawing stays accurate and the whole sequence flows without mistakes.
This helps anyone creating 3D scenes from single images, like in virtual reality or gaming. It makes the generated videos more consistent and requires less computational effort.
The abstract doesn't state specific limitations, but it notes the method is effective for 56-frame generation and with depth corruption. However, it may not address stability for arbitrarily large motions or different generation horizons beyond those tested.
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Stabilizing Camera-Controlled Novel View Synthesis at Inference Time — Prajwal Singh, Arjun Badola, Seema Kumari et al.
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.