FlexiClip: Locality-Preserving Free-Form Character Animation
- URL: http://arxiv.org/abs/2501.08676v1
- Date: Wed, 15 Jan 2025 09:07:12 GMT
- Title: FlexiClip: Locality-Preserving Free-Form Character Animation
- Authors: Anant Khandelwal,
- Abstract summary: Existing methods, such as AniClipart, effectively model geometric deformations but often fail to ensure smooth temporal transitions.<n>This paper introduces FlexiClip, a novel approach designed to overcome these limitations by addressing the intertwined challenges of temporal consistency and geometric integrity.
- Score: 14.50214193838818
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Animating clipart images with seamless motion while maintaining visual fidelity and temporal coherence presents significant challenges. Existing methods, such as AniClipart, effectively model spatial deformations but often fail to ensure smooth temporal transitions, resulting in artifacts like abrupt motions and geometric distortions. Similarly, text-to-video (T2V) and image-to-video (I2V) models struggle to handle clipart due to the mismatch in statistical properties between natural video and clipart styles. This paper introduces FlexiClip, a novel approach designed to overcome these limitations by addressing the intertwined challenges of temporal consistency and geometric integrity. FlexiClip extends traditional B\'ezier curve-based trajectory modeling with key innovations: temporal Jacobians to correct motion dynamics incrementally, continuous-time modeling via probability flow ODEs (pfODEs) to mitigate temporal noise, and a flow matching loss inspired by GFlowNet principles to optimize smooth motion transitions. These enhancements ensure coherent animations across complex scenarios involving rapid movements and non-rigid deformations. Extensive experiments validate the effectiveness of FlexiClip in generating animations that are not only smooth and natural but also structurally consistent across diverse clipart types, including humans and animals. By integrating spatial and temporal modeling with pre-trained video diffusion models, FlexiClip sets a new standard for high-quality clipart animation, offering robust performance across a wide range of visual content. Project Page: https://creative-gen.github.io/flexiclip.github.io/
Related papers
- Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better [61.381599921020175]
Temporal consistency is critical in video prediction to ensure that outputs are coherent and free of artifacts.
Traditional methods, such as temporal attention and 3D convolution, may struggle with significant object motion.
We propose the Tracktention Layer, a novel architectural component that explicitly integrates motion information using point tracks.
arXiv Detail & Related papers (2025-03-25T17:58:48Z) - EvAnimate: Event-conditioned Image-to-Video Generation for Human Animation [58.41979933166173]
EvAnimate is a framework that leverages event streams as motion cues to animate static human images.
We show that EvAnimate achieves high temporal fidelity and robust performance in scenarios where traditional video-derived cues fall short.
arXiv Detail & Related papers (2025-03-24T11:05:41Z) - SOYO: A Tuning-Free Approach for Video Style Morphing via Style-Adaptive Interpolation in Diffusion Models [54.641809532055916]
We introduce SOYO, a novel diffusion-based framework for video style morphing.
Our method employs a pre-trained text-to-image diffusion model without fine-tuning, combining attention injection and AdaIN to preserve structural consistency.
To harmonize across video frames, we propose a novel adaptive sampling scheduler between two style images.
arXiv Detail & Related papers (2025-03-10T07:27:01Z) - TVG: A Training-free Transition Video Generation Method with Diffusion Models [12.037716102326993]
Transition videos play a crucial role in media production, enhancing the flow and coherence of visual narratives.
Recent advances in diffusion model-based video generation offer new possibilities for creating transitions but face challenges such as poor inter-frame relationship modeling and abrupt content changes.
We propose a novel training-free Transition Video Generation (TVG) approach using video-level diffusion models that addresses these limitations without additional training.
arXiv Detail & Related papers (2024-08-24T00:33:14Z) - AniClipart: Clipart Animation with Text-to-Video Priors [28.76809141136148]
We introduce AniClipart, a computational system that converts static animations into high-quality animations guided by text-to-video prior.<n>By differentiable As-Rigid-As-Possible (ARAP) shape deformation and differentiable rendering, AniClipart can be end-to-end optimized while maintaining rigidity.
arXiv Detail & Related papers (2024-04-18T17:24:28Z) - LatentMan: Generating Consistent Animated Characters using Image Diffusion Models [44.18315132571804]
We propose a zero-shot approach for generating consistent videos of animated characters based on Text-to-Image (T2I) diffusion models.
Our proposed approach outperforms existing zero-shot T2V approaches in generating videos of animated characters in terms of pixel-wise consistency and user preference.
arXiv Detail & Related papers (2023-12-12T10:07:37Z) - VMC: Video Motion Customization using Temporal Attention Adaption for
Text-to-Video Diffusion Models [58.93124686141781]
Video Motion Customization (VMC) is a novel one-shot tuning approach crafted to adapt temporal attention layers within video diffusion models.
Our approach introduces a novel motion distillation objective using residual vectors between consecutive frames as a motion reference.
We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts.
arXiv Detail & Related papers (2023-12-01T06:50:11Z) - MagicAnimate: Temporally Consistent Human Image Animation using
Diffusion Model [74.84435399451573]
This paper studies the human image animation task, which aims to generate a video of a certain reference identity following a particular motion sequence.
Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion.
We introduce MagicAnimate, a diffusion-based framework that aims at enhancing temporal consistency, preserving reference image faithfully, and improving animation fidelity.
arXiv Detail & Related papers (2023-11-27T18:32:31Z) - DynIBaR: Neural Dynamic Image-Based Rendering [79.44655794967741]
We address the problem of synthesizing novel views from a monocular video depicting a complex dynamic scene.
We adopt a volumetric image-based rendering framework that synthesizes new viewpoints by aggregating features from nearby views.
We demonstrate significant improvements over state-of-the-art methods on dynamic scene datasets.
arXiv Detail & Related papers (2022-11-20T20:57:02Z) - Learning Fine-Grained Motion Embedding for Landscape Animation [140.57889994591494]
We propose a model named FGLA to generate high-quality and realistic videos by learning Fine-Grained motion embedding.
To train and evaluate on diverse time-lapse videos, we build the largest high-resolution Time-lapse video dataset with Diverse scenes.
Our method achieves relative improvements by 19% on LIPIS and 5.6% on FVD compared with state-of-the-art methods on our dataset.
arXiv Detail & Related papers (2021-09-06T02:47:11Z) - Deep Animation Video Interpolation in the Wild [115.24454577119432]
In this work, we formally define and study the animation video code problem for the first time.
We propose an effective framework, AnimeInterp, with two dedicated modules in a coarse-to-fine manner.
Notably, AnimeInterp shows favorable perceptual quality and robustness for animation scenarios in the wild.
arXiv Detail & Related papers (2021-04-06T13:26:49Z) - Going beyond Free Viewpoint: Creating Animatable Volumetric Video of
Human Performances [7.7824496657259665]
We present an end-to-end pipeline for the creation of high-quality animatable volumetric video content of human performances.
Semantic enrichment and geometric animation ability are achieved by establishing temporal consistency in the 3D data.
For pose editing, we exploit the captured data as much as possible and kinematically deform the captured frames to fit a desired pose.
arXiv Detail & Related papers (2020-09-02T09:46:12Z)
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.