SkyReels-A1: Expressive Portrait Animation in Video Diffusion Transformers

• URL: http://arxiv.org/abs/2502.10841v1
• Date: Sat, 15 Feb 2025 16:08:40 GMT
• Title: SkyReels-A1: Expressive Portrait Animation in Video Diffusion Transformers
• Authors: Di Qiu, Zhengcong Fei, Rui Wang, Jialin Bai, Changqian Yu, Mingyuan Fan, Guibin Chen, Xiang Wen,
• Abstract summary: We present SkyReels-A1, a framework built upon video diffusion Transformer to facilitate portrait image animation. SkyReels-A1 capitalizes on the strong generative capabilities of video DiT, enhancing facial motion transfer precision, identity retention, and temporal coherence. It is highly applicable to domains such as virtual avatars, remote communication, and digital media generation.
• Score: 30.06494915665044
• License:
• Abstract: We present SkyReels-A1, a simple yet effective framework built upon video diffusion Transformer to facilitate portrait image animation. Existing methodologies still encounter issues, including identity distortion, background instability, and unrealistic facial dynamics, particularly in head-only animation scenarios. Besides, extending to accommodate diverse body proportions usually leads to visual inconsistencies or unnatural articulations. To address these challenges, SkyReels-A1 capitalizes on the strong generative capabilities of video DiT, enhancing facial motion transfer precision, identity retention, and temporal coherence. The system incorporates an expression-aware conditioning module that enables seamless video synthesis driven by expression-guided landmark inputs. Integrating the facial image-text alignment module strengthens the fusion of facial attributes with motion trajectories, reinforcing identity preservation. Additionally, SkyReels-A1 incorporates a multi-stage training paradigm to incrementally refine the correlation between expressions and motion while ensuring stable identity reproduction. Extensive empirical evaluations highlight the model's ability to produce visually coherent and compositionally diverse results, making it highly applicable to domains such as virtual avatars, remote communication, and digital media generation.

Related papers

• Dynamic Concepts Personalization from Single Videos [92.62863918003575]
  We introduce Set-and-Sequence, a novel framework for personalizing generative video models with dynamic concepts. Our approach imposes a-temporal weight space within an architecture that does not explicitly separate spatial and temporal features. Our framework embeds dynamic concepts into the video model's output domain, enabling unprecedented editability and compositionality.
  arXiv  Detail & Related papers  (2025-02-20T18:53:39Z)
• X-Dyna: Expressive Dynamic Human Image Animation [49.896933584815926]
  X-Dyna is a zero-shot, diffusion-based pipeline for animating a single human image. It generates realistic, context-aware dynamics for both the subject and the surrounding environment.
  arXiv  Detail & Related papers  (2025-01-17T08:10:53Z)
• GoHD: Gaze-oriented and Highly Disentangled Portrait Animation with Rhythmic Poses and Realistic Expression [33.886734972316326]
  GoHD is a framework designed to produce highly realistic, expressive, and controllable portrait videos from any reference identity with any motion. An animation module utilizing latent navigation is introduced to improve the generalization ability across unseen input styles. A conformer-structured conditional diffusion model is designed to guarantee head poses that are aware of prosody. A two-stage training strategy is devised to decouple frequent and frame-wise lip motion distillation from the generation of other more temporally dependent but less audio-related motions.
  arXiv  Detail & Related papers  (2024-12-12T14:12:07Z)
• UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics [74.10447111842504]
  UniReal is a unified framework designed to address various image generation and editing tasks. Inspired by recent video generation models, we propose a unifying approach that treats image-level tasks as discontinuous video generation. Although designed for image-level tasks, we leverage videos as a scalable source for universal supervision.
  arXiv  Detail & Related papers  (2024-12-10T18:59:55Z)
• Hallo3: Highly Dynamic and Realistic Portrait Image Animation with Diffusion Transformer Networks [25.39030226963548]
  We introduce the first application of a pretrained transformer-based video generative model for portrait animation. Our method is validated through experiments on benchmark and newly proposed wild datasets.
  arXiv  Detail & Related papers  (2024-12-01T08:54:30Z)
• AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding [24.486705010561067]
  The paper introduces AniTalker, a framework designed to generate lifelike talking faces from a single portrait. AniTalker effectively captures a wide range of facial dynamics, including subtle expressions and head movements.
  arXiv  Detail & Related papers  (2024-05-06T02:32:41Z)
• Zero-shot High-fidelity and Pose-controllable Character Animation [89.74818983864832]
  Image-to-video (I2V) generation aims to create a video sequence from a single image. Existing approaches suffer from inconsistency of character appearances and poor preservation of fine details. We propose PoseAnimate, a novel zero-shot I2V framework for character animation.
  arXiv  Detail & Related papers  (2024-04-21T14:43:31Z)
• FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio [45.71036380866305]
  We abstract the process of people hearing speech, extracting meaningful cues, and creating dynamically audio-consistent talking faces from a single audio. Specifically, it involves two critical challenges: one is to effectively decouple identity, content, and emotion from entangled audio, and the other is to maintain intra-video diversity and inter-video consistency. We introduce the Controllable Coherent Frame generation, which involves the flexible integration of three trainable adapters with frozen Latent Diffusion Models.
  arXiv  Detail & Related papers  (2024-03-04T09:59:48Z)
• From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos [88.08209394979178]
  Dynamic facial expression recognition (DFER) in the wild is still hindered by data limitations. We introduce a novel Static-to-Dynamic model (S2D) that leverages existing SFER knowledge and dynamic information implicitly encoded in extracted facial landmark-aware features.
  arXiv  Detail & Related papers  (2023-12-09T03:16:09Z)
• FAAC: Facial Animation Generation with Anchor Frame and Conditional Control for Superior Fidelity and Editability [14.896554342627551]
  We introduce a facial animation generation method that enhances both face identity fidelity and editing capabilities. This approach incorporates the concept of an anchor frame to counteract the degradation of generative ability in original text-to-image models. Our method's efficacy has been validated on multiple representative DreamBooth and LoRA models.
  arXiv  Detail & Related papers  (2023-12-06T02:55:35Z)
• DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors [63.43133768897087]
  We propose a method to convert open-domain images into animated videos. The key idea is to utilize the motion prior to text-to-video diffusion models by incorporating the image into the generative process as guidance. Our proposed method can produce visually convincing and more logical & natural motions, as well as higher conformity to the input image.
  arXiv  Detail & Related papers  (2023-10-18T14:42:16Z)

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.