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CoDeF: Content Deformation Fields for Temporally Consistent Video Processing
Hao Ouyang, Qiuyu Wang, Yuxi Xiao, Qingyan Bai, Juntao Zhang, Kecheng Zheng, Xiaowei Zhou, Qifeng Chen, Yujun Shen
TL;DR
Video processing faces challenges in temporal consistency, data quality, and computational demand relative to image processing. CoDeF represents video with a canonical content field and a temporal deformation field, then lifts image algorithms by applying them to the canonical image. The paper reports improved temporal consistency and texture for translated videos, while noting per-scene optimization and extreme viewpoint changes as challenges.
Problem
Video processing has not matched image-processing progress, with temporal consistency, video texture quality, and computational demands identified as challenges.
Source: §1 · p. 2Method
CoDeF represents video with a 2D canonical image field and 3D temporal deformation field, regularizing the canonical image's semantics and applying image algorithms to it for propagation across frames.
Source: §1 · p. 2, §3 · p. 4, §3.1 · p. 4Results
CoDeF's prompt-guided video translations improve temporal consistency and texture quality over state-of-the-art zero-shot video translations with generative models.
Source: §1 · p. 2Takeaways & Limitations
The representation supports lifting image translation, super-resolution, segmentation, and keypoint detection to video processing tasks.
Source: §1 · p. 2The method requires per-scene optimization, and extreme changes in viewing points remain a challenge.
Source: §5 · p. 8
Abstract
from arXiv · showhide
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/.
1. Introduction
Video processing lags image processing amid temporal-consistency, data-quality, and compute challenges; CoDeF represents video with a canonical image and temporal deformation field to lift image algorithms. The paper reports improved reconstruction and processed-video consistency, while identifying per-scene optimization and extreme viewpoint changes as challenges.
1. Introduction
- The paper frames video-as-image representation as a response to temporal inconsistency, lower-quality video textures, and greater computational demands that contribute to a gap with image-processing quality.Neural-network randomness is identified as one challenge to temporal consistency.Source: §1 · p. 2
- Earlier video mosaics and neural layered atlases have limited detail reconstruction and can yield distorted atlases with impaired semantics, reducing the effectiveness of image-processing algorithms.Subtle motion such as blinking eyes or slight smiles may be omitted.Source: §1 · p. 2
- Approximately 4.4 higher PSNR accompanies improved reconstruction quality and canonical-image naturalness; fitting takes about 300 seconds versus more than 10 hours for prior implicit layered representations [16].The reported time is for estimating the canonical image with its deformation field.Source: §1 · p. 2
- CoDeF couples a 2D hash-based image field with a 3D temporal deformation field, using annealed hash training to balance canonical-image naturalness and reconstruction faithfulness.The deformation representation supports complex motion including water and smog; training begins with a smooth grid for coarse rigid motion and gradually adds high-frequency detail.Source: §1 · p. 2
- CoDeF lifts image translation, super-resolution, segmentation, and keypoint detection to video tasks by processing a canonical image and propagating results through learned deformation.For prompt-guided translation, ControlNet is applied to one canonical image rather than run across all frames; translation improves temporal consistency and texture quality over zero-shot video translations.Source: §1 · p. 2
2. Related Work
Related work spans implicit neural representations, consistent video editing, and generative-model video processing. CoDeF is situated among these approaches through its hash-based deformation representation and canonical image, which its figure shows as the basis for propagating image-algorithm outputs across time.
2. Related Work
- Implicit neural representations use coordinate-based MLPs for images, videos, and 3D/4D scenes; multi-resolution grids or hash tables have been explored to accelerate training, while deformation fields can fit dynamic scenes.The paper's stated objective is to reconstruct video with a semantically inheriting canonical image for video processing.Source: §2 · p. 2
- Figure 2 depicts CoDeF as a 2D canonical content field and a 3D temporal deformation field, with image-algorithm outputs propagated from the canonical image across time.The fields use multi-resolution 2D or 3D hash tables with an efficient MLP.Source: Figure 2 · p. 3
- Consistent-editing methods either propagate edits from an initial frame, risking inaccuracies with complex motion or occlusion, or use layered representations; Text2Live edits an optimized atlas with text [1].CoDeF is described as using a more semantically aware canonical representation and hash-based deformation design than Text2Live [1].Source: §2 · p. 3
- Video-processing methods built on text-to-image models explore DDIM latent spaces and cross-frame attention, but may have compromised temporal consistency and imprecise control conditions.The cited approaches include Tune-A-Video [64], Text2Video-Zero [17], FateZero [36], Vid2Vid-Zero [59], and Video-P2P [22].Source: §2 · p. 3
- Text-to-video architectures can generate frames semantically corresponding to text, but may have limited control over video conditions or low resolution under substantial computational demands.The passage names NUWA, CogVideo [12], Phenaki [55], Make-A-Video [48], Imagen Video [10], and Gen-1 [7].Source: §2 · p. 3
3. Method
CoDeF represents a video with a canonical image and a deformation field, allowing image algorithms to be applied once and propagated across frames. Its design combines hash-based fields with regularization and optional grouped representations to support reconstruction and consistent video processing.
3. Method
- Applying an image algorithm frame by frame can create inconsistencies, while adding a temporal module requires video training and does not guarantee consistency.The method instead applies the algorithm to a canonical image and propagates its effect through a learned deformation field.Source: §3 · pp. 3–4
3.1. Content Deformation Fields
- CoDeF represents video with a 2D canonical content field and a 3D temporal deformation field, implemented with hash tables and small MLPs.The deformation maps observed-frame positions to corresponding canonical positions; the canonical field represents flattened video textures.Source: §3.1 · p. 4
- Multi-resolution hash encoding lets the fields capture high-frequency image detail and complex spatiotemporal deformation.The deformation field maps each encoded position and time to its corresponding position in the canonical field.Source: §3.1 · p. 4
3.2. Model Design
- The deformation field’s fitting capacity can compromise smoothness and canonical-image semantics, so CoDeF uses annealed hash encoding and flow-based consistency.For large occlusions or complex multi-object scenes, semantic masks and grouped deformation fields are an additional option; background loss regularizes unsupervised regions.Source: §3.2 · pp. 5–6
- Training minimizes reconstruction loss with flow regularization, adding background loss when grouped deformation fields are used.The flow-guided loss encourages corresponding points in consecutive frames to map to the same canonical points.Source: §3.2 · pp. 5–6
3.3. Application to Consistent Video Processing
- After fitting, the canonical image can be resized to include more scene content and used for downstream processing or direct user edits.The described applications include prompt-guided translation, object tracking, and super-resolution, with results propagated using the learned deformation.Source: §3.3 · p. 7
4. Experiments
CoDeF improves video reconstruction over the Neural Image Atlas and lifts image-processing tasks to video. The ablations show that hash encoding, annealed hash, and flow loss affect reconstruction, canonical-image naturalness, and temporal smoothness.
4.1. Experimental Setup
- On one NVIDIA A6000, fitting 100 video frames averages approximately 5 minutes, with optimization duration varying from 1 to 10 minutes.Duration varies with video length, motion type, and number of layers.Source: §4.1 · p. 7
4.2. Evaluation
- Video reconstruction PSNR is 4.4 dB higher than Neural Image Atlas, with training taking 5 minutes versus 10 hours.The comparison reports reconstruction quality on collected video datasets.Source: §4.2 · p. 7
- CoDeF lifts image translation, keypoint and object tracking, super-resolution, and interactive editing to video with substantial temporal consistency in translation.The reported applications include object tracking, super-resolution, and single-image editing propagated across frames.Source: Figure 6 · p. 7, Figure 7 · p. 7, §4.2 · p. 8
4.3. Ablation Study
- Replacing 3D hash encoding with positional encoding lowers reconstruction PSNR by 3.1 dB; removing annealed hash yields unnatural canonical images, while removing flow loss flickers smooth regions.The unnatural canonical image is associated with multiple hands, and the figure notes that canonical-image unnaturalness harms downstream tasks.Source: §4.3 · p. 8, Figure 9 · p. 8
5. Conclusion and Discussion
CoDeF targets temporally consistent video processing and reports promising fidelity and consistency. Its discussion identifies per-scene optimization, extreme viewpoint changes, and large non-rigid deformations as remaining challenges.
5. Conclusion and Discussion
- The authors report promising results in reconstructed-video fidelity and temporal consistency.The conclusion frames these as the approach's demonstrated strengths.Source: §5 · p. 8
- Per-scene optimization remains required, and extreme viewpoint changes may benefit from added 3D prior knowledge.The authors suggest adapting feed-forward implicit-field techniques to per-scene optimization and using 3D priors for viewpoint changes.Source: §5 · p. 8
- Large non-rigid deformations remain a concern; multiple canonical images are proposed to better represent complex deformations.Source: §5 · p. 9