GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields (2011.12100v2)

Abstract: Deep generative models allow for photorealistic image synthesis at high resolutions. But for many applications, this is not enough: content creation also needs to be controllable. While several recent works investigate how to disentangle underlying factors of variation in the data, most of them operate in 2D and hence ignore that our world is three-dimensional. Further, only few works consider the compositional nature of scenes. Our key hypothesis is that incorporating a compositional 3D scene representation into the generative model leads to more controllable image synthesis. Representing scenes as compositional generative neural feature fields allows us to disentangle one or multiple objects from the background as well as individual objects' shapes and appearances while learning from unstructured and unposed image collections without any additional supervision. Combining this scene representation with a neural rendering pipeline yields a fast and realistic image synthesis model. As evidenced by our experiments, our model is able to disentangle individual objects and allows for translating and rotating them in the scene as well as changing the camera pose.

• The paper introduces a compositional generative neural feature field model that disentangles object attributes through 3D scene representations.
• It employs a hybrid neural rendering pipeline that combines 3D volume rendering with 2D upscaling to achieve fast, high-quality image synthesis.
• The approach learns from unstructured image collections, enabling precise manipulation of object poses and appearances with improved FID performance.

Essay: GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields

The paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields" by Michael Niemeyer and Andreas Geiger presents an innovative approach to controllable image synthesis leveraging deep generative models. The central proposition of the paper is that utilizing a compositional 3D scene representation within the generative model can significantly enhance controllability in the image synthesis process.

Overview

Traditional deep generative models, particularly GANs, have shown impressive capability in synthesizing photorealistic 2D images with high resolutions. However, most of the existing models operate in the 2D space, which inherently ignores the three-dimensional nature of the real world, subsequently leading to entangled representations. The authors address this issue by introducing an approach that incorporates compositional 3D scene representations into the generative framework, allowing better disentanglement of objects and providing enhanced controllability during image synthesis.

Technical Contributions

The paper's core contributions are as follows:

1. Compositional Generative Neural Feature Fields (GNFF): The model represents scenes as compositional generative neural feature fields. The generation of feature fields entails volume rendering to obtain low-resolution feature images, which are then processed by a neural rendering network to produce high-resolution RGB images. This structure allows the model to manage the shape, appearance, and pose of individual objects independently.
2. Neural Rendering Pipeline: GIRAFFE enhances the synthesis speed and realism by blending 3D volume rendering with 2D neural rendering. Specifically, the neural renderer upscales low-resolution feature maps to the desired output resolution, ensuring faster inference and high-quality imagery.
3. Unsupervised Learning from Image Collections: The proposed method learns from unstructured image collections without requiring additional supervision. This capability enables the model to disentangle objects from the background effectively and facilitates operations such as translating and rotating objects within the scene.
4. Disentanglement Mechanism: By incorporating axis-aligned positional encoding, GIRAFFE’s approach encourages canonical representation learning, simplifying the control over 3D object manipulations. The method’s compositionality also extends to multi-object scenes, which can be managed during the synthesis process.

Experimental Results

The paper provides extensive empirical validation across various datasets, including synthetic and real-world images. Notably, the model exhibits proficiency in controlling individual objects within the scene by modifying their poses and appearances and performing compound manipulations such as circular translations.

Quantitatively, the performance is evaluated using the Fréchet Inception Distance (FID), with GIRAFFE outperforming other state-of-the-art methods in most cases. For example, compared to voxel-based and other 3D-aware generative models, GIRAFFE achieves lower FID scores across diverse datasets like Cats, CelebA, and CompCars, thereby validating its superior synthesis quality and consistency.

Implications and Future Directions

The implications of this research are manifold:

• Practical Applications: The compositional 3D scene representation can revolutionize industries reliant on 3D content creation, such as gaming and film production, by offering a more controllable and efficient synthesis pipeline.
• Improved Latent Space Navigation: The method lays groundwork for better navigation and control in the latent space of generative models, enabling applications requiring fine-grained image manipulations.

Future research might explore the following avenues:

1. Learning Transformation Distributions: Current limitations related to mismatches between assumed uniform distributions and real-world distributions could be addressed by learning these distributions directly from data.
2. Integration of Supervision: Incorporating minimal supervision like predicted object masks can further enhance the model’s capability to handle complex multi-object scenes more effectively.

Conclusion

The GIRAFFE model sets a notable milestone in the domain of controllable image synthesis by advocating and implementing a compositional 3D scene representation within a generative framework. This approach not only advances the existing methodologies but also opens up new horizons in achieving more realistic and controllable image synthesis, addressing long-standing challenges effectively. The direction and outcomes of this research hold substantial promise for future advancements in AI-driven content generation.