AIHOT 于 2026-08-17 收录了“AnyTalk：利用视频生成模型为任意角色生成3D语音动画”这一公开动态。以下先呈现从来源页面抓取的正文，再给出 AIHOT 摘要与 TopoReduce 编辑解读。
PUBLIC SOURCE CONTENT
已抓取公开正文公开原文内容
[2608.16143] AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model
Computer Science > Graphics
arXiv:2608.16143 (cs)
-
[Submitted on 17 Aug 2026]
Title:AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model
Authors:Kwan Yun, Serin Yoon, Sunjin Jung, Jung Eun Yoo, Inyup Lee, Junyong Noh
View a PDF of the paper titled AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model, by Kwan Yun and 5 other authors
View PDF
HTML (experimental)
Abstract:We present AnyTalk, a novel method for generating 3D speech animations for arbitrary characters without requiring any animation data. While existing audio-driven 3D speech animation methods rely on character-specific training data or laborious rigging/re-meshing, AnyTalk circumvents these limitations by leveraging recent video diffusion models trained on extensive video datasets. We first adapt a pre-trained video diffusion model to a target character through our Character-specific Fine-tuning (\textit{CsF}) technique. By fine-tuning on rendered images of the 3D character paired with zeroed-out audio embeddings (representing "no motion"), we eliminate the need for animation data while preserving the motion prior of large-scale video diffusion model. We then uplift the resulting talking-head video into a 3D speech animation by estimating blendshape parameters through a proposed optimization process. AnyTalk enables lip-synced animations across diverse face meshes and blendshape configurations, significantly reducing manual effort and data requirements. We further enhance usability by distilling AnyTalk into a streamlined network, $\text{AnyTalk}_{RT}$, thereby enabling real-time performance. By leveraging talking-head video generation, our method broadens access to audio-driven speech animation technology for arbitrary characters. The code is publicly available at this https URL.
Comments:
accepted to TVCG, Project page at this https URL
Subjects:
Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Sound (cs.SD)
ACM classes:
I.3; I.4
Cite as:
arXiv:2608.16143 [cs.GR]
(or
arXiv:2608.16143v1 [cs.GR] for this version)
https://doi.org/10.48550/arXiv.2608.16143
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kwan Yun [view email]
[v1]
Mon, 17 Aug 2026 05:53:42 UTC (16,564 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model, by Kwan Yun and 5 other authors
- View PDF
- HTML (experimental)
- TeX Source
view license
Current browse context:
cs.GR
< prev
|
next >
new
|
recent
| 2026-08
Change to browse by:
cs
cs.CV
cs.MM
cs.SD
References & Citations
- NASA ADS
- Google Scholar
- Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
- Author
- Venue
- Institution
- Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? |
Disable MathJax (What is MathJax?)
arXiv:2608.16143 (cs)
-
[Submitted on 17 Aug 2026]
Title:AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model
Authors:Kwan Yun, Serin Yoon, Sunjin Jung, Jung Eun Yoo, Inyup Lee, Junyong Noh
View a PDF of the paper titled AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model, by Kwan Yun and 5 other authors
View PDF
HTML (experimental)
Abstract:We present AnyTalk, a novel method for generating 3D speech animations for arbitrary characters without requiring any animation data. While existing audio-driven 3D speech animation methods rely on character-specific training data or laborious rigging/re-meshing, AnyTalk circumvents these limitations by leveraging recent video diffusion models trained on extensive video datasets. We first adapt a pre-trained video diffusion model to a target character through our Character-specific Fine-tuning (\textit{CsF}) technique. By fine-tuning on rendered images of the 3D character paired with zeroed-out audio embeddings (representing "no motion"), we eliminate the need for animation data while preserving the motion prior of large-scale video diffusion model. We then uplift the resulting talking-head video into a 3D speech animation by estimating blendshape parameters through a proposed optimization process. AnyTalk enables lip-synced animations across diverse face meshes and blendshape configurations, significantly reducing manual effort and data requirements. We further enhance usability by distilling AnyTalk into a streamlined network, $\text{AnyTalk}_{RT}$, thereby enabling real-time performance. By leveraging talking-head video generation, our method broadens access to audio-driven speech animation technology for arbitrary characters. The code is publicly available at this https URL.
Comments:
accepted to TVCG, Project page at this https URL
Subjects:
Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Sound (cs.SD)
ACM classes:
I.3; I.4
Cite as:
arXiv:2608.16143 [cs.GR]
(or
arXiv:2608.16143v1 [cs.GR] for this version)
https://doi.org/10.48550/arXiv.2608.16143
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kwan Yun [view email]
[v1]
Mon, 17 Aug 2026 05:53:42 UTC (16,564 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model, by Kwan Yun and 5 other authors
- View PDF
- HTML (experimental)
- TeX Source
view license
Current browse context:
cs.GR
< prev
|
next >
new
|
recent
| 2026-08
Change to browse by:
cs
cs.CV
cs.MM
cs.SD
References & Citations
- NASA ADS
- Google Scholar
- Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
- Author
- Venue
- Institution
- Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? |
Disable MathJax (What is MathJax?)
AIHOT 摘要
AnyTalk提出一种无需动画数据即可为任意角色生成3D语音动画的新方法，通过Character-specific Fine-tuning（CsF）技术将预训练视频扩散模型适配至目标角色，再经优化过程估计blendshape参数，将说话头视频提升为3D语音动画。该方法支持多种面部网格和blendshape配置，显著降低人工与数据需求。蒸馏版AnyTalk_{RT}可实现实时性能，代码已公开。
为什么值得关注
AnyTalk用视频生成模型替代动画数据标注，其角色特定微调策略为低成本3D语音动画提供了可迁移的思路。
工程化解读
从 TopoReduce 的工程视角看，这条信息属于“论文与研究”主题。它的价值不只在于一个新产品或新观点本身，还在于说明 AI 系统正在如何影响模型接入、智能体协作、研发流程、基础设施和团队决策。实际采用前，应结合原文确认版本、适用范围、价格和运行条件。
- 发布时间：2026-08-17；AIHOT 分类：论文与研究。
- AIHOT 标签：
- AIHOT 判断：AnyTalk用视频生成模型替代动画数据标注，其角色特定微调策略为低成本3D语音动画提供了可迁移的思路。
- AIHOT 评分：54；评分用于站内排序，不等同于独立评测结论。
TopoReduce 编辑观察
当 AI 动态进入真实生产环境，团队需要同时关注能力边界、数据来源、调用成本、权限控制和可回滚性。把单条新闻放回完整工程链路中阅读，比只看标题更有助于判断它是否适合自己的产品和工作流。