Framer: Interactive Frame Interpolation

• URL: http://arxiv.org/abs/2410.18978v2
• Date: Mon, 04 Nov 2024 13:37:31 GMT
• Title: Framer: Interactive Frame Interpolation
• Authors: Wen Wang, Qiuyu Wang, Kecheng Zheng, Hao Ouyang, Zhekai Chen, Biao Gong, Hao Chen, Yujun Shen, Chunhua Shen,
• Abstract summary: Framer targets producing smoothly transitioning frames between two images as per user creativity. Our approach supports customizing the transition process by tailoring the trajectory of some selected keypoints. It is noteworthy that our system also offers an "autopilot" mode, where we introduce a module to estimate the keypoints and the trajectory automatically.
• Score: 73.06734414930227
• License:
• Abstract: We propose Framer for interactive frame interpolation, which targets producing smoothly transitioning frames between two images as per user creativity. Concretely, besides taking the start and end frames as inputs, our approach supports customizing the transition process by tailoring the trajectory of some selected keypoints. Such a design enjoys two clear benefits. First, incorporating human interaction mitigates the issue arising from numerous possibilities of transforming one image to another, and in turn enables finer control of local motions. Second, as the most basic form of interaction, keypoints help establish the correspondence across frames, enhancing the model to handle challenging cases (e.g., objects on the start and end frames are of different shapes and styles). It is noteworthy that our system also offers an "autopilot" mode, where we introduce a module to estimate the keypoints and refine the trajectory automatically, to simplify the usage in practice. Extensive experimental results demonstrate the appealing performance of Framer on various applications, such as image morphing, time-lapse video generation, cartoon interpolation, etc. The code, the model, and the interface will be released to facilitate further research.

Related papers

• Thin-Plate Spline-based Interpolation for Animation Line Inbetweening [54.69811179222127]
  Chamfer Distance (CD) is commonly adopted for evaluating inbetweening performance. We propose a simple yet effective method for animation line inbetweening that adopts thin-plate spline-based transformation. Our method outperforms existing approaches by delivering high-quality results with enhanced fluidity.
  arXiv  Detail & Related papers  (2024-08-17T08:05:31Z)
• Explore Synergistic Interaction Across Frames for Interactive Video Object Segmentation [70.93295323156876]
  We propose a framework that can accept multiple frames simultaneously and explore synergistic interaction across frames (SIAF) Our SwinB-SIAF achieves new state-of-the-art performance on DAVIS 2017 (89.6%, J&F@60) Our R50-SIAF is more than 3 faster than the state-of-the-art competitor under challenging multi-object scenarios.
  arXiv  Detail & Related papers  (2024-01-23T04:19:15Z)
• Interactive Image Segmentation with Cross-Modality Vision Transformers [18.075338835513993]
  Cross-modality vision transformers exploits mutual information to better guide the learning process. The stability of our method in term of avoiding failure cases shows its potential to be a practical annotation tool.
  arXiv  Detail & Related papers  (2023-07-05T13:29:05Z)
• Multiview Regenerative Morphing with Dual Flows [20.062713286961326]
  We propose a novel approach called Multiview Regenerative Morphing. It formulates the morphing process as an optimization to solve for rigid and optimal-transport. Our approach does not rely on user-specified correspondences or 2D/3D input meshes.
  arXiv  Detail & Related papers  (2022-08-02T07:22:48Z)
• TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation [50.49396123016185]
  Video frame (VFI) aims to synthesize an intermediate frame between two consecutive frames. We propose a novel Trajectory-aware Transformer for Video Frame Interpolation (TTVFI) Our method outperforms other state-of-the-art methods in four widely-used VFI benchmarks.
  arXiv  Detail & Related papers  (2022-07-19T03:37:49Z)
• ALANET: Adaptive Latent Attention Network forJoint Video Deblurring and Interpolation [38.52446103418748]
  We introduce a novel architecture, Adaptive Latent Attention Network (ALANET), which synthesizes sharp high frame-rate videos. We employ combination of self-attention and cross-attention module between consecutive frames in the latent space to generate optimized representation for each frame. Our method performs favorably against various state-of-the-art approaches, even though we tackle a much more difficult problem.
  arXiv  Detail & Related papers  (2020-08-31T21:11:53Z)
• Deep Sketch-guided Cartoon Video Inbetweening [24.00033622396297]
  We propose a framework to produce cartoon videos by fetching the color information from two inputs while following the animated motion guided by a user sketch. By explicitly considering the correspondence between frames and the sketch, we can achieve higher quality results than other image synthesis methods.
  arXiv  Detail & Related papers  (2020-08-10T14:22:04Z)
• All at Once: Temporally Adaptive Multi-Frame Interpolation with Advanced Motion Modeling [52.425236515695914]
  State-of-the-art methods are iterative solutions interpolating one frame at the time. This work introduces a true multi-frame interpolator. It utilizes a pyramidal style network in the temporal domain to complete the multi-frame task in one-shot.
  arXiv  Detail & Related papers  (2020-07-23T02:34:39Z)
• Image Morphing with Perceptual Constraints and STN Alignment [70.38273150435928]
  We propose a conditional GAN morphing framework operating on a pair of input images. A special training protocol produces sequences of frames, combined with a perceptual similarity loss, promote smooth transformation over time. We provide comparisons to classic as well as latent space morphing techniques, and demonstrate that, given a set of images for self-supervision, our network learns to generate visually pleasing morphing effects.
  arXiv  Detail & Related papers  (2020-04-29T10:49:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.