EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance

EPiC is a new framework for video generation that enables precise camera control without requiring point cloud or camera pose estimation. By using first-frame visibility masking to create aligned anchor videos, the approach achieves state-of-the-art results on benchmark datasets while requiring significantly fewer parameters and training resources than existing methods.

EPiC represents a meaningful advancement in video generation technology by addressing a fundamental inefficiency in current camera-control approaches. Previous methods rely on estimated point clouds and camera trajectories to render anchor videos that guide diffusion models, but estimation errors compound during training, forcing models to compensate for misalignments. This approach proves costly and inefficient. EPiC sidesteps these estimation steps entirely by masking source videos based on first-frame visibility, creating perfectly aligned training data without the computational overhead or error propagation.

The technique builds on years of research in conditional video generation and diffusion models, reflecting the broader trend toward more efficient, parameter-light architectures. By introducing Anchor-ControlNet—a lightweight module adding less than 1% of parameters to existing models—EPiC demonstrates how structured priors can enhance pretrained systems without substantial architectural changes. This aligns with industry momentum toward adapter-based and LoRA-style fine-tuning approaches that maximize efficiency.

For developers and researchers, EPiC's ability to work with in-the-wild videos and its strong zero-shot generalization to video-to-video scenarios expand practical applicability. The framework's efficiency gains matter significantly for resource-constrained environments and commercial deployment. The state-of-the-art results on RealEstate10K and MiraData establish credibility while the cross-domain generalization suggests robust underlying principles. Looking ahead, the method's compatibility with point-cloud-based systems at inference time could enable hybrid approaches combining efficiency during training with precision at deployment, potentially influencing how camera control becomes standardized in production video generation pipelines.

• →EPiC eliminates point cloud and camera pose estimation requirements by using first-frame visibility masking for precise anchor video creation
• →Anchor-ControlNet adds less than 1% additional parameters while achieving state-of-the-art performance on RealEstate10K and MiraData benchmarks
• →The framework requires substantially fewer training steps, parameters, and data compared to existing camera control methods
• →EPiC demonstrates strong zero-shot generalization to video-to-video scenarios despite training on image-to-video tasks
• →The method works with in-the-wild videos and maintains compatibility with point-cloud-informed systems at inference time