Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework
- URL: http://arxiv.org/abs/2403.16510v1
- Date: Mon, 25 Mar 2024 07:54:18 GMT
- Title: Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework
- Authors: Ziyao Huang, Fan Tang, Yong Zhang, Xiaodong Cun, Juan Cao, Jintao Li, Tong-Yee Lee,
- Abstract summary: Make-Your-Anchor is a system requiring only a one-minute video clip of an individual for training.
We finetune a proposed structure-guided diffusion model on input video to render 3D mesh conditions into human appearances.
A novel identity-specific face enhancement module is introduced to improve the visual quality of facial regions in the output videos.
- Score: 33.46782517803435
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Despite the remarkable process of talking-head-based avatar-creating solutions, directly generating anchor-style videos with full-body motions remains challenging. In this study, we propose Make-Your-Anchor, a novel system necessitating only a one-minute video clip of an individual for training, subsequently enabling the automatic generation of anchor-style videos with precise torso and hand movements. Specifically, we finetune a proposed structure-guided diffusion model on input video to render 3D mesh conditions into human appearances. We adopt a two-stage training strategy for the diffusion model, effectively binding movements with specific appearances. To produce arbitrary long temporal video, we extend the 2D U-Net in the frame-wise diffusion model to a 3D style without additional training cost, and a simple yet effective batch-overlapped temporal denoising module is proposed to bypass the constraints on video length during inference. Finally, a novel identity-specific face enhancement module is introduced to improve the visual quality of facial regions in the output videos. Comparative experiments demonstrate the effectiveness and superiority of the system in terms of visual quality, temporal coherence, and identity preservation, outperforming SOTA diffusion/non-diffusion methods. Project page: \url{https://github.com/ICTMCG/Make-Your-Anchor}.
Related papers
- Real-time One-Step Diffusion-based Expressive Portrait Videos Generation [85.07446744308247]
We introduce OSA-LCM (One-Step Avatar Latent Consistency Model), paving the way for real-time diffusion-based avatars.
Our method achieves comparable video quality to existing methods but requires only one sampling step, making it more than 10x faster.
arXiv Detail & Related papers (2024-12-18T03:42:42Z) - VividFace: A Diffusion-Based Hybrid Framework for High-Fidelity Video Face Swapping [43.30061680192465]
We present the first diffusion-based framework specifically designed for video face swapping.
Our approach incorporates a specially designed diffusion model coupled with a VidFaceVAE.
Our framework achieves superior performance in identity preservation, temporal consistency, and visual quality compared to existing methods.
arXiv Detail & Related papers (2024-12-15T18:58:32Z) - UniVST: A Unified Framework for Training-free Localized Video Style Transfer [102.52552893495475]
This paper presents UniVST, a unified framework for localized video style transfer based on diffusion model.
It operates without the need for training, offering a distinct advantage over existing diffusion methods that transfer style across entire videos.
arXiv Detail & Related papers (2024-10-26T05:28:02Z) - VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide [48.22321420680046]
VideoGuide is a novel framework that enhances the temporal consistency of pretrained text-to-video (T2V) models.
It improves temporal quality by interpolating the guiding model's denoised samples into the sampling model's denoising process.
The proposed method brings about significant improvement in temporal consistency and image fidelity.
arXiv Detail & Related papers (2024-10-06T05:46:17Z) - COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing [57.76170824395532]
Video editing is an emerging task, in which most current methods adopt the pre-trained text-to-image (T2I) diffusion model to edit the source video.
We propose COrrespondence-guided Video Editing (COVE) to achieve high-quality and consistent video editing.
COVE can be seamlessly integrated into the pre-trained T2I diffusion model without the need for extra training or optimization.
arXiv Detail & Related papers (2024-06-13T06:27:13Z) - Vivid-ZOO: Multi-View Video Generation with Diffusion Model [76.96449336578286]
New challenges lie in the lack of massive captioned multi-view videos and the complexity of modeling such multi-dimensional distribution.
We propose a novel diffusion-based pipeline that generates high-quality multi-view videos centered around a dynamic 3D object from text.
arXiv Detail & Related papers (2024-06-12T21:44:04Z) - Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models [48.56724784226513]
We propose Customize-A-Video that models the motion from a single reference video and adapts it to new subjects and scenes with both spatial and temporal varieties.
The proposed modules are trained in a staged pipeline and inferred in a plug-and-play fashion, enabling easy extensions to various downstream tasks.
arXiv Detail & Related papers (2024-02-22T18:38:48Z) - DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and
View-Change Human-Centric Video Editing [48.086102360155856]
We introduce the dynamic Neural Radiance Fields (NeRF) as the innovative video representation.
We propose the image-based video-NeRF editing pipeline with a set of innovative designs to provide consistent and controllable editing.
Our method, dubbed as DynVideo-E, significantly outperforms SOTA approaches on two challenging datasets by a large margin of 50% 95% for human preference.
arXiv Detail & Related papers (2023-10-16T17:48:10Z)
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.