AGORA is a new framework that extends 3D Gaussian Splatting with a generative adversarial network to produce high‑fidelity, animatable 3D head avatars. It introduces a lightweight FLAME‑conditioned deformation branch that predicts per‑Gaussian residuals for identity‑preserving, fine‑grained expression control, and a dual‑discriminator training scheme that enforces expression fidelity. The system achieves real‑time inference at 250 FPS on a single GPU and, for the first time, CPU‑only animatable 3DGS avatar synthesis at ~9 FPS.
By Ramazan Fazylov, Sergey Zagoruyko, Aleksandr Parkin, Stamatis Lefkimmiatis, Ivan Laptev
arXiv:2608. 19900v1 Announce Type: new Abstract: For full-body avatars, modeling surface dynamics is crucial for overcoming the uncanny valley and achieving perceptual realism.
By Guoxing Sun, Heming Zhu, Linjie Lyu, Pascal Fua, Christian Theobalt, Marc Habermann
For full-body avatars, modeling surface dynamics is crucial for overcoming the uncanny valley and achieving perceptual realism. Person-agnostic methods recover static 3D avatars from monocular images, videos, or text prompts, but their skeleton-driven animations lack realistic surface dynamics such as clothing wrinkles.
arXiv:2609.12850v1 Announce Type: new
Abstract: Accurate head modeling requires a stable yet expressive geometric representation. Existing Gaussian-based head avatars commonly rely on parametric temp...
By Lei Shi, Sen Peng, Zhiyang Deng, Zhonggui Chen, Xiaohu Guo, Baorong Yang, Xiao Dong
arXiv:2606. 30347v1 Announce Type: cross Abstract: We present FFAvatar, a Transformer-based 3D Gaussian framework for fast construction of high-quality and animatable 4D head avatars from one or more reference portrait images.
By Jianjiang Yao, Ke Xian, Renxiang Dai, Robert Caiming Qiu
arXiv:2609.39273v1 Announce Type: new
Abstract: This report presents \textbf{MegaAvatar}, a controllable talking avatar generation framework built on top of the Wan2.2-TI2V-5B model. Compared with pr...
By Junyao Gao, Sibo Liu, Weidong Zhang, Cairong Zhao, Jun Zhang
arXiv:2609.24158v1 Announce Type: new
Abstract: Reconstructing expressive and relightable 3D head avatars from monocular videos remains challenging in computer vision, as it requires accurate modelin...
By Jiankuo Zhao, Xiangyu Zhu, Jijie Li, Baiqin Wang, Shukai Chen, Zhen Lei
arXiv:2606. 31981v1 Announce Type: cross Abstract: Creating photorealistic, animatable 3D human avatars from monocular images still largely depends on Linear Blend Skinning (LBS) and parametric body models, which constrain expressivity and often introduce artifacts due to imperfect fitting.
By Peng Li, Rawal Khirodkar, Junxuan Li, Yuan Dong, Chen Cao, Yuan Liu, Wenhan Luo, Yike Guo, Shunsuke Saito
arXiv:2512.03593v2 Announce Type: replace
Abstract: We present a CloseUpAvatar - a novel approach for articulated human avatar representation supporting a wider range of camera motions, while preserv...
By David Svitov, Pietro Morerio, Lourdes Agapito, Alessio Del Bue
arXiv:2606.24232v2 Announce Type: replace
Abstract: We introduce FiCA, a Feed-forward, instant Gaussian Codec Avatar generation pipeline that creates lifelike avatars from a single portrait image. Ge...
By Kim Youwang, Zhengyu Yang, Liuhao Ge, Yu Rong, Timur Bagautdinov, Su Zhaoen, Nir Sopher, Jovan Popovi\'c, Teng Deng, Tae-Hyun Oh, Chen Cao
NBAvatar is a method for realistic rendering of head avatars that handles non‑rigid deformations caused by hand‑face interaction. It introduces a hybrid implicit‑explicit representation, combining explicit oriented planar primitives with implicit neural rendering, and uses a geometry‑aware training scheme to jointly optimize these representations. The approach achieves up to 53% LPIPS reduction compared to Gaussian‑based avatar methods, improves PSNR and SSIM, and surpasses the state‑of‑the‑art InteractAvatar in structural similarity for novel‑view and novel‑pose rendering.
By David Svitov, Mahtab Dahaghin, Pietro Morerio, Alessio Del Bue
arXiv:2606. 06903v1 Announce Type: cross Abstract: Human image animation aims to generate a video from a static reference image, guided by pose information extracted from a driving video.
By Yuan Zeng, Yujia Shi, Yuhao Yang, Dongxia Liu, Zongqing Lu, Wenming Yang, Qingmin Liao