LightMotion: A Light and Tuning-free Method for Simulating Camera Motion in Video Generation

TL;DR

The paper tackles the heavy computational burden of camera-motion conditioned video generation by introducing LightMotion, a tuning-free method operating entirely in latent space. It breakup the problem into three components: latent space permutation to simulate panning/zooming/rotation, background-aware latent-space resampling with cross-frame alignment to fill new perspectives coherently, and latent space correction to mitigate SNR shifts during updates. Through exhaustive experiments against multiple baselines, LightMotion demonstrates superior quantitative metrics (FVD, CLIP-F, CLIP-T) and qualitative coherence, while supporting diverse user-defined motion parameters. The approach enables end-to-end, accessible camera-motion video generation without fine-tuning or depth estimation, with practical implications for film, VR, and content creation pipelines.

Abstract

Existing camera motion-controlled video generation methods face computational bottlenecks in fine-tuning and inference. This paper proposes LightMotion, a light and tuning-free method for simulating camera motion in video generation. Operating in the latent space, it eliminates additional fine-tuning, inpainting, and depth estimation, making it more streamlined than existing methods. The endeavors of this paper comprise: (i) The latent space permutation operation effectively simulates various camera motions like panning, zooming, and rotation. (ii) The latent space resampling strategy combines background-aware sampling and cross-frame alignment to accurately fill new perspectives while maintaining coherence across frames. (iii) Our in-depth analysis shows that the permutation and resampling cause an SNR shift in latent space, leading to poor-quality generation. To address this, we propose latent space correction, which reintroduces noise during denoising to mitigate SNR shift and enhance video generation quality. Exhaustive experiments show that our LightMotion outperforms existing methods, both quantitatively and qualitatively.

Figures (15)

• Figure 1: LightMotion enables video generation with camera motion through user-defined parameter combinations without fine-tuning.
• Figure 2: Comparisons with existing methods. (a) Animatediff AnimateDiff produces fixed viewpoints videos. (b) MotionCtrl MotionCtrl fine-tunes Animatediff to achieve camera control. (c) LightMotion allows Animatediff to simulate camera motions without fine-tuning.
• Figure 3: The overall pipeline of LightMotion. It first performs general denoising, $t = T \to T_0$. The original latent is updated through latent space permutation and resampling. Then, it continues denoising, $t = T_0 \to T_1$, to preserve semantic integrity and camera motion. Next, the SNR shift is corrected by the diffusion process, $t = T_1 \to T_2$. Finally, the general denoising continues, $t = T_2 \to 1$.
• Figure 4: An illustration of permutation and resampling in latent space with different camera motions: (a) panning, (b) zooming, and (c) rotation. Different numbers represent distinct positions, which are rearranged into new coordinates via camera motions, followed by sampling from the old perspective to obtain the new one. Additionally, (d) presents a toy example for the cross-frame alignment strategy.
• Figure 5: Results of different sampling methods: (a) Random sampling results in object repetition. (b) Background-aware sampling results in artifacts. (c) Background-aware sampling with cross-frame alignment generates accurate results without artifacts.