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...
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.
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...
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.
arXiv:2609.38343v1 Announce Type: new Abstract: We present SInGA, a novel method for learning Semantic Inpainting for animatable Gaussian head Avatars from a single image. Existing avatar approaches...
arXiv:2605. 04035v3 Announce Type: replace-cross Abstract: We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups.
arXiv:2610.27712v1 Announce Type: cross Abstract: Feed-forward 3D Gaussian Splatting (FF3DGS) reconstructs 3D scenes from images in a single forward pass, making it well-suited to applications that m...
F4Splat introduces a feed‑forward predictive densification strategy for 3D Gaussian splatting that allocates Gaussians based on a densification‑score guided by spatial complexity and multi‑view overlap. The method predicts per‑region scores to estimate required Gaussian density, enabling explicit control over the total Gaussian budget without retraining. This adaptive allocation reduces redundancy in simple regions and minimizes duplicate Gaussians across overlapping views, yielding compact yet high‑quality 3D representations and superior novel‑view synthesis performance with fewer Gaussians.
Spackle is a lightweight residual learning framework designed to improve large-view single-image novel view synthesis (NVS) by mitigating capacity competition in hybrid decoupled systems that combine 3D Gaussian Splatting (3DGS) and diffusion models. It operates in three stages: predicting base 3DGS attributes, automatically identifying poorly reconstructed regions, and learning a residual 3DGS focused on those areas. During inference, Spackle merges the baseline and augmented Gaussians to produce high-fidelity novel views, achieving state‑of‑the‑art performance on large-view-deviation cases.
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.
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.
arXiv:2610.20590v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) methods enable efficient reconstruction of 3D scenes from sparse multi-view images in a single forward pass,...