CamCtrl3D: Single-Image Scene Exploration with Precise 3D Camera Control
This work addresses scene exploration for applications like virtual reality or content creation, but it is incremental as it builds on existing methods like MotionCtrl and CameraCtrl.
The authors tackled the problem of generating fly-through videos from a single image and a camera trajectory by building upon an image-to-video latent diffusion model, achieving state-of-the-art results with a calibrated dataset for scale consistency.
We propose a method for generating fly-through videos of a scene, from a single image and a given camera trajectory. We build upon an image-to-video latent diffusion model. We condition its UNet denoiser on the camera trajectory, using four techniques. (1) We condition the UNet's temporal blocks on raw camera extrinsics, similar to MotionCtrl. (2) We use images containing camera rays and directions, similar to CameraCtrl. (3) We reproject the initial image to subsequent frames and use the resulting video as a condition. (4) We use 2D<=>3D transformers to introduce a global 3D representation, which implicitly conditions on the camera poses. We combine all conditions in a ContolNet-style architecture. We then propose a metric that evaluates overall video quality and the ability to preserve details with view changes, which we use to analyze the trade-offs of individual and combined conditions. Finally, we identify an optimal combination of conditions. We calibrate camera positions in our datasets for scale consistency across scenes, and we train our scene exploration model, CamCtrl3D, demonstrating state-of-theart results.