TIGaussian: Disentangle Gaussians for Spatial-Awared Text-Image-3D Alignment

• URL: http://arxiv.org/abs/2601.19247v1
• Date: Tue, 27 Jan 2026 06:30:32 GMT
• Title: TIGaussian: Disentangle Gaussians for Spatial-Awared Text-Image-3D Alignment
• Authors: Jiarun Liu, Qifeng Chen, Yiru Zhao, Minghua Liu, Baorui Ma, Sheng Yang,
• Abstract summary: TIGaussian harnesses 3D Gaussian Splatting (3DGS) characteristics to strengthen cross-modality alignment.<n>Our multi-branch 3DGS tokenizer decouples the intrinsic properties of 3DGS structures into compact latent representations.<n>A text-3D projection module adaptively maps 3D features to text embedding space for better text-3D alignment.
• Score: 58.46706158310462
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While visual-language models have profoundly linked features between texts and images, the incorporation of 3D modality data, such as point clouds and 3D Gaussians, further enables pretraining for 3D-related tasks, e.g., cross-modal retrieval, zero-shot classification, and scene recognition. As challenges remain in extracting 3D modal features and bridging the gap between different modalities, we propose TIGaussian, a framework that harnesses 3D Gaussian Splatting (3DGS) characteristics to strengthen cross-modality alignment through multi-branch 3DGS tokenizer and modality-specific 3D feature alignment strategies. Specifically, our multi-branch 3DGS tokenizer decouples the intrinsic properties of 3DGS structures into compact latent representations, enabling more generalizable feature extraction. To further bridge the modality gap, we develop a bidirectional cross-modal alignment strategies: a multi-view feature fusion mechanism that leverages diffusion priors to resolve perspective ambiguity in image-3D alignment, while a text-3D projection module adaptively maps 3D features to text embedding space for better text-3D alignment. Extensive experiments on various datasets demonstrate the state-of-the-art performance of TIGaussian in multiple tasks.

Related papers

• Joint Semantic and Rendering Enhancements in 3D Gaussian Modeling with Anisotropic Local Encoding [86.55824709875598]
  We propose a joint enhancement framework for 3D semantic Gaussian modeling that synergizes both semantic and rendering branches.<n>Unlike conventional point cloud shape encoding, we introduce an anisotropic 3D Gaussian Chebyshev descriptor to capture fine-grained 3D shape details.<n>We employ a cross-scene knowledge transfer module to continuously update learned shape patterns, enabling faster convergence and robust representations.
  arXiv  Detail & Related papers  (2026-01-05T18:33:50Z)
• C3G: Learning Compact 3D Representations with 2K Gaussians [55.04010158339562]
  Recent approaches use per-pixel 3D Gaussian Splatting for reconstruction, followed by a 2D-to-3D feature lifting stage for scene understanding.<n>We propose C3G, a novel feed-forward framework that estimates compact 3D Gaussians only at essential spatial locations.
  arXiv  Detail & Related papers  (2025-12-03T17:59:05Z)
• Enhanced Cross-modal 3D Retrieval via Tri-modal Reconstruction [4.820576346277399]
  Cross-modal 3D retrieval is a critical yet challenging task, aiming to achieve bi-directional retrieval between 3D and text modalities.<n>We propose to adopt multi-view images and point clouds to jointly represent 3D shapes, facilitating tri-modal alignment.<n>Our method significantly outperforms previous state-of-the-art methods in both shape-to-text and text-to-shape retrieval tasks.
  arXiv  Detail & Related papers  (2025-04-02T08:29:42Z)
• UniGS: Unified Language-Image-3D Pretraining with Gaussian Splatting [68.37013525040891]
  We propose UniGS, integrating 3D Gaussian Splatting (3DGS) into multi-modal pre-training to enhance the 3D representation.<n>We demonstrate the effectiveness of UniGS in learning a more general and stronger aligned multi-modal representation.
  arXiv  Detail & Related papers  (2025-02-25T05:10:22Z)
• F3D-Gaus: Feed-forward 3D-aware Generation on ImageNet with Cycle-Aggregative Gaussian Splatting [35.625593119642424]
  This paper tackles the problem of generalizable 3D-aware generation from monocular datasets.<n>We propose a novel feed-forward pipeline based on pixel-aligned Gaussian Splatting.<n>We also introduce a self-supervised cycle-aggregative constraint to enforce cross-view consistency in the learned 3D representation.
  arXiv  Detail & Related papers  (2025-01-12T04:44:44Z)
• CLIP-GS: Unifying Vision-Language Representation with 3D Gaussian Splatting [88.24743308058441]
  We present CLIP-GS, a novel multimodal representation learning framework grounded in 3DGS.<n>We develop an efficient way to generate triplets of 3DGS, images, and text, facilitating CLIP-GS in learning unified multimodal representations.
  arXiv  Detail & Related papers  (2024-12-26T09:54:25Z)
• Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding [59.51535163599723]
  FreeGS is an unsupervised semantic-embedded 3DGS framework that achieves view-consistent 3D scene understanding without the need for 2D labels.<n>FreeGS performs comparably to state-of-the-art methods while avoiding the complex data preprocessing workload.
  arXiv  Detail & Related papers  (2024-11-29T08:52:32Z)
• 3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features [70.50665869806188]
  3DiffTection is a state-of-the-art method for 3D object detection from single images. We fine-tune a diffusion model to perform novel view synthesis conditioned on a single image. We further train the model on target data with detection supervision.
  arXiv  Detail & Related papers  (2023-11-07T23:46:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.