CamPilot: Improving Camera Control in Video Diffusion Model with Efficient Camera Reward Feedback
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Recent research enhances camera-controlled video diffusion models, tackling limitations in camera controllability. The study introduces an efficient 3D decoder that transforms video latent and camera pose into 3D representations, optimizing pixel-level consistency for improved alignment. This method addresses existing reward model deficiencies and reduces computational overhead, showing effectiveness on RealEstate10K and WorldScore benchmarks. For more details, visit the [CamPilot page](https://a-bigbao.github.io/CamPilot/).
CamPilot Introduces Efficient Camera Reward Feedback for Enhanced Video Diffusion Models
Researchers have introduced CamPilot, an innovative approach that leverages Reward Feedback Learning (ReFL) to enhance camera controllability in video generation. This method addresses persistent challenges in aligning video with camera inputs.
The CamPilot team developed an efficient camera-aware 3D decoder that translates video latent into 3D representations for reward quantization. This model uses the camera pose as both an input and a projection parameter, reducing geometric distortions that can lead to blurry renderings.
They optimized pixel-level consistency between rendered views and actual ground-truth images as a reward mechanism, introducing a visibility term to selectively supervise deterministic regions through geometric warping.
Experiments on the RealEstate10K and WorldScore benchmarks show marked improvements in camera controllability and video quality, highlighting the potential of CamPilot in video generation.
For more information, visit the CamPilot Project Page.
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📰 Original Source: https://arxiv.org/abs/2601.16214v1
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