Animating Pictures with Eulerian Motion Fields (2011.15128v1)

Abstract: In this paper, we demonstrate a fully automatic method for converting a still image into a realistic animated looping video. We target scenes with continuous fluid motion, such as flowing water and billowing smoke. Our method relies on the observation that this type of natural motion can be convincingly reproduced from a static Eulerian motion description, i.e. a single, temporally constant flow field that defines the immediate motion of a particle at a given 2D location. We use an image-to-image translation network to encode motion priors of natural scenes collected from online videos, so that for a new photo, we can synthesize a corresponding motion field. The image is then animated using the generated motion through a deep warping technique: pixels are encoded as deep features, those features are warped via Eulerian motion, and the resulting warped feature maps are decoded as images. In order to produce continuous, seamlessly looping video textures, we propose a novel video looping technique that flows features both forward and backward in time and then blends the results. We demonstrate the effectiveness and robustness of our method by applying it to a large collection of examples including beaches, waterfalls, and flowing rivers.

• The paper introduces a novel method using deep learning and Eulerian motion fields to animate fluid motion in static images, generating dynamic looping videos.
• A technique called symmetric splatting leverages forward and backward warps to fill gaps and reduce artifacts in animated features, preserving image quality.
• The method significantly outperforms previous approaches in motion accuracy and visual fidelity, opening new possibilities for content creation and multimedia applications.

Animating Pictures with Eulerian Motion Fields: An Expert Overview

The paper "Animating Pictures with Eulerian Motion Fields" by Holynski et al. introduces a novel approach for turning static images into dynamic looping videos. The focus of the method is on scenes characterized by continuous fluid motion, such as those involving water and smoke. This process involves synthesizing motion fields from static images using deep learning, and subsequently animating these images with minimal human intervention.

Overview of Methodology

The proposed technique operates by leveraging Eulerian motion fields, characterized as 2D flow fields stipulating immediate particle velocity at a specific location. Unlike the Lagrangian perspective, which tracks individual particles over time, the Eulerian approach used here provides a static description of motion.

The methodology hinges on an image-to-image translation neural network, which is trained using a comprehensive dataset of videos with fluid motion to encode motion priors. This process synthesizes a plausible motion field for a new image, effectively predicting how particles would move from one location to another. The results are used to animate scenes by applying these motion fields in a deep feature domain, rather than directly warping colors, thereby preserving image texture and quality while mitigating common warping artifacts such as shearing or stretching.

Key Contributions

• Motion Representation: The paper's primary contribution is the motion representation using static Eulerian flow fields, which are integrated to generate a trajectory over time. This representation allows for dynamic texture animation while maintaining a single-frame motion field, which obfuscates the need for recurrent estimation and reduces issues of long-term distortion.
• Symmetric Splatting for Animation: The paper introduces symmetric splatting, a technique for effectively filling gaps that occur when animated pixels vacate their initial positions. It leverages a dual-splatting strategy where the feature map from the source image is warped in both forward and backward directions. These two sequences are composited, resulting in enhanced feature completeness and reduced temporal artifacts.
• Seamless Video Looping: The paper proposes a looping method that ensures animated videos loop without perceptible interruptions. This is achieved by alpha-blending features from the start and end of the computed video sequence in feature space, rather than via post-processed crossfades, thereby preventing double edges and ghosting.

Evaluation and Implications

Holynski et al. extensively evaluate their method against the work by Endo et al. and several ablated versions of their method, with considerations of both synthesized motion accuracy and visual quality. Quantitatively, their approach achieves lower endpoint error and higher fidelity in frame prediction metrics (e.g., PSNR and SSIM), underscoring the effectiveness of symmetric splatting and the Eulerian approach.

The implications of this research are substantial, particularly in the field of computer vision applications related to content creation and multimedia. By automating the process of animating photographs, this technique can expand the capabilities and accessibility of media production. It also suggests a broader application in augmented reality systems and interactive environments, where realistic animations based on limited input data are essential.

Future Developments

The paper opens avenues for further enhancing animation techniques derived from static imagery. Future work could involve extending this approach to different types of motion beyond fluid dynamics or improving model adaptation in scenes that feature occlusions or complex interactions. Moreover, integrating more sophisticated priors or leveraging larger, more diverse datasets could refine motion synthesis, enabling even finer granularity in motion details.

In conclusion, "Animating Pictures with Eulerian Motion Fields" presents a rigorous method of synthesizing dynamic textures in still images. The blend of static Eulerian motion fields with deep warping techniques demonstrates the potential of leveraging learned motion priors for generating high-quality looping animations, marking a significant step forward in the field of single-image animation.