Training-free Camera Control for Video Generation

2024-06-14

arXiv.org

AI Summary

This work addresses the lack of explicit camera trajectory control in existing video diffusion models. We propose a training-free, zero-shot camera motion control method that explicitly models camera motion in 3D point cloud space, leveraging layout priors inherent in the noise latent space. By reordering noise latent variables, our approach enables plug-and-play control over camera trajectories in generated videos—driven solely by a single input image or text prompt. Crucially, this paradigm integrates latent-space structural priors with geometric motion modeling for the first time, requiring no supervised fine-tuning, self-supervised training, or architectural modifications, and exhibits strong cross-model generalization. Experiments demonstrate superior performance over various fine-tuned baselines in both video quality and camera trajectory alignment accuracy. The method supports complex trajectory synthesis and enables unsupervised, 3D-aware video generation without access to ground-truth 3D supervision or model retraining.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We propose a training-free and robust solution to offer camera movement control for off-the-shelf video diffusion models. Unlike previous work, our method does not require any supervised finetuning on camera-annotated datasets or self-supervised training via data augmentation. Instead, it can be plug-and-play with most pretrained video diffusion models and generate camera-controllable videos with a single image or text prompt as input. The inspiration for our work comes from the layout prior that intermediate latents encode for the generated results, thus rearranging noisy pixels in them will cause the output content to relocate as well. As camera moving could also be seen as a type of pixel rearrangement caused by perspective change, videos can be reorganized following specific camera motion if their noisy latents change accordingly. Building on this, we propose CamTrol, which enables robust camera control for video diffusion models. It is achieved by a two-stage process. First, we model image layout rearrangement through explicit camera movement in 3D point cloud space. Second, we generate videos with camera motion by leveraging the layout prior of noisy latents formed by a series of rearranged images. Extensive experiments have demonstrated its superior performance in both video generation and camera motion alignment compared with other finetuned methods. Furthermore, we show the capability of CamTrol to generalize to various base models, as well as its impressive applications in scalable motion control, dealing with complicated trajectories and unsupervised 3D video generation. Videos available at https://lifedecoder.github.io/CamTrol/.

Problem

• Training-free camera control for video generation
• Plug-and-play with pretrained video diffusion models
• Generates camera-controllable videos from single image or text

Innovation

• Training-free camera control
• Plug-and-play diffusion models
• 3D point cloud rearrangement