GHARP is a two‑stage method that animates 3D human heads in real time using a large‑scale reconstruction prior. The first offline identity stage builds a compact Gaussian representation of a subject’s geometry and appearance, while the lightweight runtime animation stage predicts expression‑dependent residuals in a semantically structured latent space. A body alignment network further refines the animation by aligning body pose and clothing, enabling state‑of‑the‑art quality on the Ava‑256 benchmark with significantly faster performance and fewer Gaussians.
By Ali Benlalah, Sepehr Johari, Patricia Vitoria, Armin Kappeler, Artem Sevastopolsky, Alexander Jung, Gabriele Fanelli, Kevin Mader, Manuel Breitenstein, Claudia Pl\"uss, Jan R\"uegg, Simon Biland, Thomas Etterlin, Dmitry Kostiaev, Mathias Deschler, Brian Amberg, Sebastian Martin
The paper introduces GALA, a distillation technique that replaces costly neural decoding in 3D Gaussian avatars with a shallow MLP predicting blendshape coefficients, enabling real‑time animation. By constructing a basis via block‑local PCA under a rendering‑aware metric, GALA achieves high fidelity while reducing memory usage. Experiments on three avatar models show up to three orders of magnitude lower CPU cost and frame rates up to 60fps on mobile devices.
By Ramazan Fazylov, 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
GSHeadRelight introduces a fast relighting method for 3D Gaussian head synthesis, addressing the need for photorealistic rendering, real‑time performance, and generalization to unseen subjects in portrait photography, VR, and AR. The approach uses a unified neural radiance transfer representation that linearly responds to incident light, relying only on in‑the‑wild images rather than expensive light stage data. It achieves state‑of‑the‑art relighting results while delivering a rendering speed 12× faster than prior 3D‑aware portrait relighting methods.
By Henglei Lv, Bailin Deng, Jianzhu Guo, Xiaoqiang Liu, Pengfei Wan, Di Zhang, Lin Gao
OMEGA-Avatar is a feed‑forward framework that creates a 360° complete, animatable 3D Gaussian head from a single image. It introduces a semantic‑aware mesh deformation module to improve hair modeling while preserving topology, and a multi‑view feature splatting module that builds a shared canonical UV representation for consistent, high‑frequency detail across all viewpoints. Experiments show it outperforms existing baselines in full‑head completeness and identity preservation.
By Zehao Xia, Yiqun Wang, Zhengda Lu, Kai Liu, Jun Xiao, Peter Wonka
Habitat-GS is a navigation-focused embodied AI simulator that extends Habitat‑Sim by incorporating 3D Gaussian Splatting (3DGS) for real‑time photorealistic rendering and scalable asset import. It introduces a Gaussian avatar module that represents dynamic humans as both photorealistic visual entities and navigation obstacles, enabling agents to learn human‑aware behaviors. Experiments show that agents trained on 3DGS scenes generalize better across domains and that the avatar system supports effective human‑aware navigation while maintaining system scalability.
By Ziyuan Xia, Jingyi Xu, Chong Cui, Yuanhong Yu, Jiazhao Zhang, Qingsong Yan, Tao Ni, Junbo Chen, Xiaowei Zhou, Hujun Bao, Ruizhen Hu, Sida Peng
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:2610.09821v1 Announce Type: new
Abstract: Generative 3D Gaussian head avatars provide high-quality, efficient rendering, but synthesising the Gaussian representation remains computationally exp...
By Umar Farooq, Jean-Yves Guillemaut, Adrian Hilton, Marco Volino
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
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
Video Diffusion Avatars (VDAs) fine‑tune video diffusion models to generate photo‑realistic, multi‑view consistent avatars from a single image, allowing precise control over camera viewpoint, facial expression, and upper‑body pose. VDAs replace traditional 3D morphable models with a learned expression space, overcoming viewpoint bias through disentanglement and enabling independent control of expression, pose, and viewpoint via proxy‑mesh renderings. The method trains efficiently—34 hours on a single A100—and outperforms state‑of‑the‑art single‑image 3D avatars, improving novel‑view reenactment metrics and producing physically plausible hair dynamics, shadows, reflections, and refractions.
By Simon Giebenhain, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Zhe Chen, Matthias Nie{\ss}ner