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: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: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:2610.02207v1 Announce Type: cross
Abstract: 3D Gaussian avatars support fast rendering, however, their real-time animation is often challenged by the costly neural inference. We address this bo...
By Ramazan Fazylov, Stamatis Lefkimmiatis, Ivan Laptev
We present Generative Relightable Avatars (GRA), a person-specific method for photorealistic free-view rendering and environment-map relighting of full-body humans. We postulate that modeling fine-grained appearance details is inherently a one-to-many problem that can benefit from a generative formulation.
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
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
The paper introduces AvaImg, a multi‑stage optimization pipeline that achieves high‑fidelity SMPL(-X)+D registrations with UV texture for arbitrary clothed scans. By enforcing a body‑inside‑clothing constraint through signed winding numbers and employing a three‑level efficiency cascade, AvaImg significantly reduces runtime and storage while recovering fine surface detail via coarse‑to‑fine displacement optimization. The resulting textured registrations are nearly indistinguishable from scans, and encoding the UV maps with a frozen FLUX VAE demonstrates compatibility with 2D generative models, enabling 3D avatar generation using image‑based priors.
By Margaret Kostyrko, Yuxuan Xue, Garvita Tiwari, Gerard Pons-Moll
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:2609.36937v1 Announce Type: cross
Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation,...
By Sangeyl Lee, Seunghyun Shin, Seungho Park, Wooseok Jeon, Hae-Gon Jeon
AESplat is a new pose‑free feed‑forward 3D Gaussian Splatting framework that improves rendering quality by decoupling view‑independent and view‑dependent appearance modeling. It directly extracts the base view‑independent appearance from input images and predicts higher‑order spherical harmonic coefficients with a shallow MLP that incorporates 3D‑aware inductive biases. Experiments on several datasets show AESplat outperforms state‑of‑the‑art methods, achieving up to 0.8 dB higher PSNR than NAS3R and 1.1 dB over DepthSplat on RealEstate10K.
By Shiwei Ren, Zhiang Liu, Yongchun Fang, Hongwei Chen