2Xplat: Two Experts Are Better Than One Generalist
Hwasik Jeong, Seungryong Lee, Gyeongjin Kang, Seungkwon Yang, Xiangyu Sun, Seungtae Nam, Eunbyung Park

TL;DR
2Xplat introduces a modular two-expert framework for 3D Gaussian Splatting that separates geometry estimation from appearance synthesis, leading to faster training and superior performance compared to monolithic models.
Contribution
The paper proposes a novel two-expert architecture for pose-free 3D Gaussian Splatting, explicitly decoupling geometry and appearance modeling for improved results.
Findings
Outperforms prior pose-free methods in fewer than 5K iterations
Achieves performance comparable to state-of-the-art pose-based methods
Challenges the all-in-one paradigm with a modular approach
Abstract
Pose-free feed-forward 3D Gaussian Splatting (3DGS) has opened a new frontier for rapid 3D modeling, enabling high-quality Gaussian representations to be generated from uncalibrated multi-view images in a single forward pass. The dominant approach in this space adopts unified monolithic architectures, often built on geometry-centric 3D foundation models, to jointly estimate camera poses and synthesize 3DGS representations within a single network. While architecturally streamlined, such "all-in-one" designs may be suboptimal for high-fidelity 3DGS generation, as they entangle geometric reasoning and appearance modeling within a shared representation. In this work, we introduce 2Xplat, a pose-free feed-forward 3DGS framework based on a two-expert design that explicitly separates geometry estimation from Gaussian generation. A dedicated geometry expert first predicts camera poses, which…
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Taxonomy
Topics3D Shape Modeling and Analysis · Face recognition and analysis · Advanced Vision and Imaging
