HawkI: Homography & Mutual Information Guidance for 3D-free Single Image to Aerial View

• URL: http://arxiv.org/abs/2311.15478v3
• Date: Wed, 14 Aug 2024 22:39:41 GMT
• Title: HawkI: Homography & Mutual Information Guidance for 3D-free Single Image to Aerial View
• Authors: Divya Kothandaraman, Tianyi Zhou, Ming Lin, Dinesh Manocha,
• Abstract summary: We present HawkI, for synthesizing aerial-view images from text and an exemplar image. HawkI blends the visual features from the input image within a pretrained text-to-2Dimage stable diffusion model. At inference, HawkI employs a unique mutual information guidance formulation to steer the generated image towards faithfully replicating the semantic details of the input-image.
• Score: 67.8213192993001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present HawkI, for synthesizing aerial-view images from text and an exemplar image, without any additional multi-view or 3D information for finetuning or at inference. HawkI uses techniques from classical computer vision and information theory. It seamlessly blends the visual features from the input image within a pretrained text-to-2Dimage stable diffusion model with a test-time optimization process for a careful bias-variance trade-off, which uses an Inverse Perspective Mapping (IPM) homography transformation to provide subtle cues for aerialview synthesis. At inference, HawkI employs a unique mutual information guidance formulation to steer the generated image towards faithfully replicating the semantic details of the input-image, while maintaining a realistic aerial perspective. Mutual information guidance maximizes the semantic consistency between the generated image and the input image, without enforcing pixel-level correspondence between vastly different viewpoints. Through extensive qualitative and quantitative comparisons against text + exemplar-image based methods and 3D/ multi-view based novel-view synthesis methods on proposed synthetic and real datasets, we demonstrate that our method achieves a significantly better bias-variance trade-off towards generating high fidelity aerial-view images.Code and data is available at https://github.com/divyakraman/HawkI2024.

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