Single-Image Cinemagraphs Using Surface Normal Map Reshading
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Solution Overview
Problem
Conventional systems for creating cinemagraphs from a single image are inefficient, inaccurate, and inflexible, particularly when animating manipulable objects like clothing in the wind, due to the computational burden of training on large datasets and the inaccuracy of warping-based solutions.
Innovation Solution
A cyclic animation system that operates in the gradient domain using surface normal maps to generate looped videos, employing a cyclic neural network to synthesize geometric and appearance variations, and utilizes intrinsic image decomposition for reshading to maintain the underlying geometry and texture of manipulable objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems train on large datasets to create cinemagraphs, then accuracy improves, but computational cost and time increase significantly
Solution Approach 1:
The system pre-computes and stores surface normal maps and intrinsic image decompositions of input images before animation generation. This preliminary processing enables the neural network to work with pre-extracted geometric and appearance features during training, reducing the need for real-time computation on large datasets and thereby decreasing training time while maintaining accuracy.
Solution Approach 2:
The system extracts surface normal maps and intrinsic image components (albedo, shading, geometry) from input images as separate representations. By taking out these key geometric and appearance features, the system can train on a smaller subset of data while capturing the essential information needed for accurate cinemagraph generation, thus reducing training computational burden.
2Ease of operation
If warping-based solutions are used to animate images, then motion effect is achieved, but geometric accuracy and realism deteriorate
Solution Approach 1:
The system introduces surface normal maps as an intermediary representation between the input image and the animated output. Instead of directly warping pixel colors, the system operates in the normal map domain to compute geometric transformations, then reshades the results using intrinsic image decomposition. This intermediary approach preserves geometric accuracy while achieving realistic motion effects.
Solution Approach 2:
The system transitions from operating directly in the RGB pixel space to operating in the surface normal map space (a different dimensional representation). By lifting the problem to the normal map domain, the system can compute geometrically accurate transformations that project back to realistic pixel-space animations, resolving the accuracy issue of conventional warping methods.
3Reliability
If manual authoring by artists is used to create cinemagraphs, then quality and control improve, but efficiency and productivity worsen
Solution Approach 1:
The system enables automatic cinemagraph generation from input images without requiring manual artist intervention. The neural network model, trained on synthetic data and using pre-computed surface normal maps, autonomously performs the animation synthesis process. This self-service capability maintains quality through consistent application of learned transformations while dramatically improving efficiency compared to manual authoring.
Solution Approach 2:
The system uses synthetic data copies during training to learn animation transformations, then applies these learned patterns to real input images. By training on synthetic counterparts and copying the learned geometric and appearance relationships, the system achieves high-quality results without requiring artists to manually create each animation, thereby improving productivity.
4Ease of manufacture
If the system processes images in RGB space, then appearance changes are straightforward, but geometric consistency and normal map accuracy worsen
Solution Approach 1:
The system performs geometric processing and normal map computations in a separate dimensional space (surface normal map domain) rather than directly manipulating RGB pixels. This dimensional separation allows appearance changes to be handled through reshading operations while maintaining geometric consistency through normal map transformations, resolving the trade-off between ease of appearance manipulation and normal map accuracy.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media that utilizes neural networks to generate cinemagraphs from single RGB images. For example, the cyclic animation system includes a cyclic animation neural network trained with synthetic data, wherein different wind effects can be replicated using physically based simulations to create cyclic videos more efficiently. More specifically, the cyclic animation system generalizes a solution by operating in the gradient domain and using surface normal maps. Because normal maps are invariant to appearance (color, texture, illumination, etc.), the gap between synthetic and real data distribution in the normal map space is smaller than in the RGB space. The cyclic animation system performs a reshading approach that synthesizes RGB pixels from the original image and the animated normal maps to create plausible changes to the real image to create the cinemagraph.