InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images (2207.11148v1)

Abstract: We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm, where we sample and rendering virtual camera trajectories, including cyclic ones, allowing our model to learn stable view generation from a collection of single views. At test time, despite never seeing a video during training, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse content. We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality.

• The paper introduces a self-supervised view synthesis method that simulates cyclic camera trajectories to train on single images.
• It employs adversarial training with balanced GAN sampling and progressive trajectory growth to ensure stable, realistic frame generation over long sequences.
• The method outperforms traditional multi-view techniques on metrics like FID and KID, paving the way for immersive VR content creation.

InfiniteNature-Zero: Toward Unbounded 3D Scene Synthesis from Single Images

The paper "InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images" presents a novel approach to generating extended sequences of views of natural landscapes starting solely from a single photographic input. Authored by Zhengqi Li et al. from institutions including Google Research and UC Berkeley, this work addresses the challenge of generating realistic flythrough videos of landscapes without needing posed multi-view data or camera trajectories during training.

Core Contributions

The methodology centers around a self-supervised learning paradigm that circumvents the necessity for multi-view sequences and camera pose data, which traditionally hinder the scalability of similar tasks. To achieve this, the authors introduce innovative techniques that utilize only single-photo collections for training. Two critical aspects of their approach include:

1. Self-Supervised View Synthesis: This is achieved through simulating cyclic camera trajectories, where the model is exposed to virtual camera paths beginning and ending at the same initial image. By employing virtual views, the training process generates inputs analogous to those required for sequence prediction, thus providing a robust self-supervised target.
2. Adversarial Perpetual View Generation: The method employs adversarial training over long virtual camera trajectories. This is enhanced by introducing balanced GAN sampling and progressive trajectory growth techniques, which stabilize training dynamics and facilitate the generation of stable, realistic frames over extensive sequences.

Results

The proposed InfiniteNature-Zero achieves considerable performance improvements over contemporary methods that rely on multi-view video data. Evaluations on public datasets, namely the Aerial Coastline Imagery Dataset (ACID) and the Landscape High Quality (LHQ) collection, demonstrate the method’s ability to surpass supervised methods in terms of generating realistic and consistent frames over long trajectories. Quantitative assessments, such as FID, KID, and style loss metrics, validate its superior visual fidelity and stylistic consistency.

Implications and Future Directions

The research implies significant advancements in content creation for virtual reality environments, allowing artists and developers to synthesize expansive landscape sequences unsupervised in three dimensions. From a practical standpoint, this removes logistical barriers tied to capturing large datasets of nature sequences or estimating accurate camera poses.

Theoretically, this research opens potential avenues for exploring more sophisticated 3D scene understanding from single-view inputs, paving the way for developments in unsupervised learning methodologies that can exploit vast collections of unstructured image data from the internet.

Future developments could address limitations related to ensuring global consistency of dynamic foreground elements simultaneously with background novelty, potentially incorporating techniques from emerging generative frameworks like VQ-VAE and diffusion models. Further integration of 3D world modeling capabilities could enable even more robust scene exploration scenarios akin to genuine natural landscapes.

Conclusively, InfiniteNature-Zero establishes a compelling foundation for perpetual view generation that leverages single images for an unbounded, immersive exploration of natural terrains, reiterating the transformative potentials lying within self-supervised learning strategies in computer vision and graphics.