CoDeF: Content Deformation Fields for Temporally Consistent Video Processing (2308.07926v2)

Abstract: We present the content deformation field CoDeF as a new type of video representation, which consists of a canonical content field aggregating the static contents in the entire video and a temporal deformation field recording the transformations from the canonical image (i.e., rendered from the canonical content field) to each individual frame along the time axis. Given a target video, these two fields are jointly optimized to reconstruct it through a carefully tailored rendering pipeline. We advisedly introduce some regularizations into the optimization process, urging the canonical content field to inherit semantics (e.g., the object shape) from the video. With such a design, CoDeF naturally supports lifting image algorithms for video processing, in the sense that one can apply an image algorithm to the canonical image and effortlessly propagate the outcomes to the entire video with the aid of the temporal deformation field. We experimentally show that CoDeF is able to lift image-to-image translation to video-to-video translation and lift keypoint detection to keypoint tracking without any training. More importantly, thanks to our lifting strategy that deploys the algorithms on only one image, we achieve superior cross-frame consistency in processed videos compared to existing video-to-video translation approaches, and even manage to track non-rigid objects like water and smog. Project page can be found at https://qiuyu96.github.io/CoDeF/.

• The paper introduces a two-field system that splits video content into a canonical content field and a 3D temporal deformation field for enhanced reconstruction fidelity.
• It employs multi-resolution hash tables with efficient MLPs and regularization strategies such as annealed hash encoding and flow-guided consistency loss to ensure semantic precision.
• CoDeF enables applications in video-to-video translation, object tracking, super-resolution, and interactive editing by effectively adapting image processing techniques for video.

CoDeF: Content Deformation Fields for Temporally Consistent Video Processing

The representation of video data has encountered numerous challenges, particularly in maintaining temporal consistency and reconstruction fidelity. The concept of Content Deformation Fields (CoDeF) offers a novel approach, aiming to bridge the gap between efficient video processing and the robust application of established image algorithms. The paper "CoDeF: Content Deformation Fields for Temporally Consistent Video Processing" (2308.07926) introduces a groundbreaking video representation methodology that addresses these core challenges through a two-field system.

Methodology

Canonical Content and Temporal Deformation Fields

The proposed video representation, termed CoDeF, consists of two distinct components: the canonical content field and the temporal deformation field. The canonical field aggregates static contents across an entire video into a 2D framework, optimizing semantic inheritance such as object shapes. The 3D temporal deformation field, conversely, records transformations pertinent to each video frame. Implemented through multi-resolution hash tables facilitated by efficient MLPs, these fields yield a video representation that supports the lifting of image processing algorithms to video processing tasks.

Figure 1: Illustration of the proposed video representation, black, which factorizes an arbitrary video into a 2D content canonical field and a 3D temporal deformation field.

Optimization and Regularization

To achieve maximal reconstruction fidelity and semantic correctness, CoDeF integrates regularization strategies within its optimization pipeline. The training procedure includes annealed hash encoding, fostering a balanced representation between canonical image naturalness and reconstruction fidelity. This is coupled with a flow-guided consistency loss, ensuring the temporal deformation field maintains smoothness across frames.

Figure 2: Ablation paper on the effectiveness of annealed hash. The unnaturalness in the canonical image will harm the performance of downstream tasks.

Grouped Content Deformation Fields

For complex scenarios involving multiple overlapping objects or significant occlusions, CoDeF incorporates semantic masks, facilitating enhanced object separation and deformation modeling. This grouped field technique further refines the canonical and deformation fields' robustness, ensuring accurate video reconstruction.

Applications

Video-to-Video Translation

The primary application of CoDeF is in video-to-video translation, leveraging ControlNet for prompt-guided transformations. CoDeF supports high-fidelity output and temporal consistency, outperforming traditional methods like Text2Live and diffusion-based video translation approaches.

Figure 3: Qualitative comparison on the task of text-guided video-to-video translation across different methods.

Video Object Tracking and Super-Resolution

CoDeF enables seamless lifting of image algorithms to video contexts. Utilizing SAM for segmentation and R-ESRGAN for super-resolution, the framework effectively propagates processing across video frames, ensuring high-quality, consistent outputs.

Figure 4: Video object tracking results achieved by lifting an image segmentation algorithm.

Figure 5: Video super-resolution results achieved by lifting an image super-resolution algorithm.

User-Interactive Video Editing

Notably, CoDeF permits user interactive video editing, allowing modifications to a single canonical image to be translated throughout the video sequence with temporal consistency.

Figure 6: User interactive video editing achieved by editing only one image and propagating the outcomes along the time axis.

Implications and Future Directions

The introduction of CoDeF represents a significant stride in achieving temporally consistent video processing. This methodology offers implications for enhancing real-time video editing tools and developing efficient video communication technologies. The prospect of expanding CoDeF toward feed-forward implicit fields and integrating 3D priors marks a promising trajectory for future research.

In conclusion, CoDeF delineates a pivotal framework that empowers video content to leverage sophisticated image processing algorithms, setting a foundation for continued innovation in temporally coherent video synthesis and editing.