PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models

• URL: http://arxiv.org/abs/2312.13964v3
• Date: Mon, 25 Mar 2024 05:18:04 GMT
• Title: PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models
• Authors: Yiming Zhang, Zhening Xing, Yanhong Zeng, Youqing Fang, Kai Chen,
• Abstract summary: PIA excels in aligning with condition images, achieving motion controllability by text, and the compatibility with various personalized T2I models without specific tuning. A key component of PIA is the introduction of the condition module, which utilizes the condition frame and inter-frame affinity as input to transfer appearance information.
• Score: 14.657472801570284
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in personalized text-to-image (T2I) models have revolutionized content creation, empowering non-experts to generate stunning images with unique styles. While promising, adding realistic motions into these personalized images by text poses significant challenges in preserving distinct styles, high-fidelity details, and achieving motion controllability by text. In this paper, we present PIA, a Personalized Image Animator that excels in aligning with condition images, achieving motion controllability by text, and the compatibility with various personalized T2I models without specific tuning. To achieve these goals, PIA builds upon a base T2I model with well-trained temporal alignment layers, allowing for the seamless transformation of any personalized T2I model into an image animation model. A key component of PIA is the introduction of the condition module, which utilizes the condition frame and inter-frame affinity as input to transfer appearance information guided by the affinity hint for individual frame synthesis in the latent space. This design mitigates the challenges of appearance-related image alignment within and allows for a stronger focus on aligning with motion-related guidance.

Related papers

• MagicTailor: Component-Controllable Personalization in Text-to-Image Diffusion Models [51.1034358143232]
  We introduce component-controllable personalization, a novel task that pushes the boundaries of text-to-image (T2I) models. To overcome these challenges, we design MagicTailor, an innovative framework that leverages Dynamic Masked Degradation (DM-Deg) to dynamically perturb undesired visual semantics.
  arXiv  Detail & Related papers  (2024-10-17T09:22:53Z)
• Imagine yourself: Tuning-Free Personalized Image Generation [39.63411174712078]
  We introduce Imagine yourself, a state-of-the-art model designed for personalized image generation. It operates as a tuning-free model, enabling all users to leverage a shared framework without individualized adjustments. Our study demonstrates that Imagine yourself surpasses the state-of-the-art personalization model, exhibiting superior capabilities in identity preservation, visual quality, and text alignment.
  arXiv  Detail & Related papers  (2024-09-20T09:21:49Z)
• Layout-and-Retouch: A Dual-stage Framework for Improving Diversity in Personalized Image Generation [40.969861849933444]
  We propose a novel P-T2I method called Layout-and-Retouch, consisting of two stages: 1) layout generation and 2) retouch. In the first stage, our step-blended inference utilizes the inherent sample diversity of vanilla T2I models to produce diversified layout images. In the second stage, multi-source attention swaps the context image from the first stage with the reference image, leveraging the structure from the context image and extracting visual features from the reference image.
  arXiv  Detail & Related papers  (2024-07-13T05:28:45Z)
• Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation [81.45400849638347]
  In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image containing translations in target language. In this paper, we propose an end-to-end IIMT model consisting of four modules. Our model achieves competitive performance compared to cascaded models with only 70.9% of parameters, and significantly outperforms the pixel-level end-to-end IIMT model.
  arXiv  Detail & Related papers  (2024-07-03T08:15:39Z)
• OSTAF: A One-Shot Tuning Method for Improved Attribute-Focused T2I Personalization [9.552325786494334]
  We introduce a novel parameter-efficient one-shot fine-tuning method for personalized text-to-image (T2I) personalization. A novel hypernetwork-powered attribute-focused fine-tuning mechanism is employed to achieve the precise learning of various attribute features. Our method shows significant superiority in attribute identification and application, as well as achieves a good balance between efficiency and output quality.
  arXiv  Detail & Related papers  (2024-03-17T01:42:48Z)
• Direct Consistency Optimization for Compositional Text-to-Image Personalization [73.94505688626651]
  Text-to-image (T2I) diffusion models, when fine-tuned on a few personal images, are able to generate visuals with a high degree of consistency. We propose to fine-tune the T2I model by maximizing consistency to reference images, while penalizing the deviation from the pretrained model.
  arXiv  Detail & Related papers  (2024-02-19T09:52:41Z)
• AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning [92.33690050667475]
  AnimateDiff is a framework for animating personalized T2I models without requiring model-specific tuning. We propose MotionLoRA, a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns. Results show that our approaches help these models generate temporally smooth animation clips while preserving the visual quality and motion diversity.
  arXiv  Detail & Related papers  (2023-07-10T17:34:16Z)
• TaleCrafter: Interactive Story Visualization with Multiple Characters [49.14122401339003]
  This paper proposes a system for generic interactive story visualization. It is capable of handling multiple novel characters and supporting the editing of layout and local structure. The system comprises four interconnected components: story-to-prompt generation (S2P), text-to-generation (T2L), controllable text-to-image generation (C-T2I) and image-to-video animation (I2V)
  arXiv  Detail & Related papers  (2023-05-29T17:11:39Z)
• Key-Locked Rank One Editing for Text-to-Image Personalization [43.195870616558935]
  We present Perfusion, a T2I personalization method that addresses challenges using dynamic rank-1 updates to the underlying T2I model. Perfusion avoids overfitting by introducing a new mechanism that "locks" new concepts' cross-attention Keys to their superordinate category. We show that Perfusion outperforms strong baselines in both qualitative and quantitative terms.
  arXiv  Detail & Related papers  (2023-05-02T17:56:06Z)
• Make It Move: Controllable Image-to-Video Generation with Text Descriptions [69.52360725356601]
  TI2V task aims at generating videos from a static image and a text description. To address these challenges, we propose a Motion Anchor-based video GEnerator (MAGE) with an innovative motion anchor structure. Experiments conducted on datasets verify the effectiveness of MAGE and show appealing potentials of TI2V task.
  arXiv  Detail & Related papers  (2021-12-06T07:00:36Z)

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.