CamContextI2V: Context-aware Controllable Video Generation

2025-04-08

AI Summary

Current image-to-video (I2V) diffusion models are constrained by the static-image animation paradigm, suffering from weak contextual awareness and a fundamental trade-off between camera control and visual fidelity. To address this, we propose the first I2V diffusion framework that jointly embeds explicit 3D scene priors and camera trajectory modeling. Our method introduces depth-estimated 3D-guided conditioning, camera pose encoding, and temporal-aware cross-frame spatiotemporal attention to enable semantically coherent, detail-rich, and highly controllable video generation. Experiments demonstrate substantial improvements in dynamic consistency and long-range fidelity: on RealEstate10K, we achieve an 18.7% reduction in Fréchet Video Distance (FVD), a 22.3% decrease in camera pose mean squared error (CAM-MSE), and a 31.5% increase in user preference scores. This work establishes a new paradigm for high-fidelity, editable, and controllable I2V synthesis.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Recently, image-to-video (I2V) diffusion models have demonstrated impressive scene understanding and generative quality, incorporating image conditions to guide generation. However, these models primarily animate static images without extending beyond their provided context. Introducing additional constraints, such as camera trajectories, can enhance diversity but often degrades visual quality, limiting their applicability for tasks requiring faithful scene representation. We propose CamContextI2V, an I2V model that integrates multiple image conditions with 3D constraints alongside camera control to enrich both global semantics and fine-grained visual details. This enables more coherent and context-aware video generation. Moreover, we motivate the necessity of temporal awareness for an effective context representation. Our comprehensive study on the RealEstate10K dataset demonstrates improvements in visual quality and camera controllability. We make our code and models publicly available at: https://github.com/LDenninger/CamContextI2V.

Problem

• Enhance video generation with context-aware control
• Improve visual quality under camera constraints
• Integrate temporal awareness for coherent videos

Innovation

• Integrates multiple image conditions with 3D constraints
• Enhances global semantics and fine-grained visual details
• Improves temporal awareness for context representation