Training-free Camera Control for Video Generation (2406.10126v4)

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/.

• The paper proposes CamTrol, a training-free method that leverages latent layout priors in diffusion models to precisely control camera movements in video generation.
• It models image layout rearrangement via explicit 3D point cloud manipulation to generate dynamic 3D rotation videos without additional training.
• Extensive experiments validate that utilizing inherent diffusion model structures can achieve robust and efficient video synthesis with controlled camera motions.

The topic of "Training-free Camera Control for Video Generation" is addressed through advancements in video diffusion models and control mechanisms that allow for precise manipulation of camera movements without the need for extensive training data or finetuning. Two significant papers that contribute to this field are especially notable.

The paper "Training-free Camera Control for Video Generation" proposes a novel method called CamTrol, which offers a robust solution for controlling camera movements in video diffusion models without requiring supervised finetuning on camera-annotated datasets or self-supervised training. CamTrol leverages the concept of layout priors inherent in the intermediate latents of diffusion models. By rearranging noisy pixels, the model achieves video reorganization following specific camera motions. This process is implemented in two stages: first, modeling image layout rearrangement through explicit camera movement in 3D point cloud space, and second, generating videos with camera motion using the layout priors formed by a series of rearranged images. Extensive experiments demonstrate the method's robustness and its capability to generate impressive 3D rotation videos with dynamic content (2406.10126).

Another prominent work is "ControlVideo: Training-free Controllable Text-to-Video Generation," which addresses the challenges in text-driven video generation, such as appearance inconsistency and structural flickers in long videos. ControlVideo adapts from ControlNet and introduces modules for cross-frame interaction in self-attention to ensure appearance coherence, frame interpolation to smooth flicker effects, and a hierarchical sampler to generate long videos efficiently. These modules collectively enable natural and efficient text-to-video generation without the need for additional training, facilitating control over the camera and motion aspects of generated videos (2305.13077).

Together, these works underscore the potential of training-free approaches in enabling more flexible, efficient, and effective control over camera movement in video generation. They highlight how leveraging existing structures within diffusion models can lead to significant advancements without the heavy computational and data burdens typically associated with training models for such tasks.