Boosting Camera Motion Control for Video Diffusion Transformers

• URL: http://arxiv.org/abs/2410.10802v1
• Date: Mon, 14 Oct 2024 17:58:07 GMT
• Title: Boosting Camera Motion Control for Video Diffusion Transformers
• Authors: Soon Yau Cheong, Duygu Ceylan, Armin Mustafa, Andrew Gilbert, Chun-Hao Paul Huang,
• Abstract summary: We show that transformer-based diffusion models (DiT) suffer from severe degradation in camera motion accuracy. To address the persistent motion degradation in DiT, we introduce Camera Motion Guidance (CMG), which boosts camera control by over 400%. Our method universally applies to both U-Net and DiT models, offering improved camera control for video generation tasks.
• Score: 21.151900688555624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in diffusion models have significantly enhanced the quality of video generation. However, fine-grained control over camera pose remains a challenge. While U-Net-based models have shown promising results for camera control, transformer-based diffusion models (DiT)-the preferred architecture for large-scale video generation - suffer from severe degradation in camera motion accuracy. In this paper, we investigate the underlying causes of this issue and propose solutions tailored to DiT architectures. Our study reveals that camera control performance depends heavily on the choice of conditioning methods rather than camera pose representations that is commonly believed. To address the persistent motion degradation in DiT, we introduce Camera Motion Guidance (CMG), based on classifier-free guidance, which boosts camera control by over 400%. Additionally, we present a sparse camera control pipeline, significantly simplifying the process of specifying camera poses for long videos. Our method universally applies to both U-Net and DiT models, offering improved camera control for video generation tasks.

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