CamCtrl3D: Single-Image Scene Exploration with Precise 3D Camera Control (2501.06006v2)

Abstract: 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.

• The paper introduces CamCtrl3D, integrating innovative 3D conditioning into stable video diffusion for enhanced single-image fly-through video generation.
• It employs raw camera extrinsics, ray and direction conditioning, and 2D-to-3D transformers to achieve robust spatial fidelity and temporal consistency.
• Evaluation on datasets like ScanNet and RealEstate10K shows up to an 8x reduction in FVD and improved PSNR, underscoring significant quality gains.

Analysis of "CamCtrl3D: Single-Image Scene Exploration with Precise 3D Camera Control"

The paper presents an advanced method, CamCtrl3D, for generating high-quality fly-through videos from a single image, augmented with a predefined camera trajectory. This approach builds upon the Stable Video Diffusion (SVD) framework, incorporating precise 3D camera controls to enable realistic scene exploration. The authors leverage several conditioning strategies to significantly enhance video generation fidelity, with a focus on adapting existing video model priors to the single-image context.

Methodology Overview

The core enhancement in CamCtrl3D lies in its innovative conditioning mechanisms applied to the UNet denoiser within the SVD model. By introducing several novel conditioning techniques, the authors aim to deliver improved scene exploration capabilities from minimal initial data.

1. Raw Camera Extrinsics Conditioning: The model conditions on raw camera extrinsics, utilizing a residual block in the temporal layers of the UNet. This method facilitates the learning of 3D spatial relationships and their impact on sequential frames.
2. Camera Rays and Directions Conditioning: Incorporating camera rays and directions images directly into the model provides pixel-level control over video generation. This condition enriches the temporal processing with spatial information encoded in the camera parameters.
3. Initial Image Re-projection: By reprojecting the initial image across frames using estimated depth information and camera poses, the model ensures that visible surfaces in the initial frame maintain high fidelity across the sequence.
4. 2D↔3D Transformers for Global Representation: Sparse 2D↔3D transformers introduce explicit 3D scene reasoning into the video generation process, allowing the model to synthesize coherent frames based on a global 3D feature understanding.

The combination of these techniques within a ControlNet-style framework optimally leverages the individual strengths of each conditioning approach, resulting in improved fidelity and consistency in generated videos.

Results and Evaluation

The proposed CamCtrl3D model undergoes rigorous evaluation against established datasets like ScanNet, RealEstate10K, and DL3DV. Significant improvements are reported over baselines and existing methods such as MotionCtrl and 4DiM, with CamCtrl3D achieving up to 8 times reduction in Fréchet Video Distance (FVD) and substantial gains in PSNR metrics. These numerical results underline CamCtrl3D's capability to maintain both aesthetic quality and structural detail during fly-through sequences.

The authors employ a carefully designed metric that assesses both overall video quality and the model's capacity to preserve initial image details, offering a comprehensive evaluation of video generation integrity.

Implications and Future Work

The development of CamCtrl3D represents a meaningful advancement in single-image video generation, particularly for applications in computer vision and graphics where minimal input data is available. The approach has potential implications for interactive media, virtual reality, and digital content creation, offering tools to transform static images into immersive, navigable video experiences.

Future research could explore enhancing the model's adaptability to dynamic scenes, potentially by incorporating datasets with moving objects and further tuning the model's capacity to simulate realistic motion. Additionally, extending the framework to generate longer sequences effectively remains an area for experimental exploration, gradually improving the scalability of CamCtrl3D's methods.

In conclusion, CamCtrl3D demonstrates a proficient blend of state-of-the-art video diffusion models and innovative 3D conditioning techniques, setting a new benchmark in transforming static images into comprehensive video narratives.