Debiasing Diffusion Priors via 3D Attention for Consistent Gaussian Splatting

• URL: http://arxiv.org/abs/2512.07345v2
• Date: Tue, 16 Dec 2025 14:43:37 GMT
• Title: Debiasing Diffusion Priors via 3D Attention for Consistent Gaussian Splatting
• Authors: Shilong Jin, Haoran Duan, Litao Hua, Wentao Huang, Yuan Zhou,
• Abstract summary: We propose a novel framework, TD-Attn, which addresses multi-view inconsistency via two key components.<n> TD-Attn has the potential to serve as a universal plugin, significantly enhancing multi-view consistency across 3D tasks.
• Score: 10.320439319212463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Versatile 3D tasks (e.g., generation or editing) that distill from Text-to-Image (T2I) diffusion models have attracted significant research interest for not relying on extensive 3D training data. However, T2I models exhibit limitations resulting from prior view bias, which produces conflicting appearances between different views of an object. This bias causes subject-words to preferentially activate prior view features during cross-attention (CA) computation, regardless of the target view condition. To overcome this limitation, we conduct a comprehensive mathematical analysis to reveal the root cause of the prior view bias in T2I models. Moreover, we find different UNet layers show different effects of prior view in CA. Therefore, we propose a novel framework, TD-Attn, which addresses multi-view inconsistency via two key components: (1) the 3D-Aware Attention Guidance Module (3D-AAG) constructs a view-consistent 3D attention Gaussian for subject-words to enforce spatial consistency across attention-focused regions, thereby compensating for the limited spatial information in 2D individual view CA maps; (2) the Hierarchical Attention Modulation Module (HAM) utilizes a Semantic Guidance Tree (SGT) to direct the Semantic Response Profiler (SRP) in localizing and modulating CA layers that are highly responsive to view conditions, where the enhanced CA maps further support the construction of more consistent 3D attention Gaussians. Notably, HAM facilitates semantic-specific interventions, enabling controllable and precise 3D editing. Extensive experiments firmly establish that TD-Attn has the potential to serve as a universal plugin, significantly enhancing multi-view consistency across 3D tasks.

Related papers

• SPAN: Spatial-Projection Alignment for Monocular 3D Object Detection [49.12928389918159]
  Existing monocular 3D detectors typically tame the pronounced nonlinear regression of 3D bounding box through decoupled prediction paradigm.<n>We propose novel Spatial-Projection Alignment (SPAN) with two pivotal components.<n>SPAN enforces an explicit global spatial constraint between the predicted and ground-truth 3D bounding boxes, thereby rectifying spatial drift caused by decoupled attribute regression.<n>3D-2D Projection Alignment ensures that the projected 3D box is aligned tightly within its corresponding 2D detection bounding box on the image plane, mitigating projection misalignment overlooked in previous works.
  arXiv  Detail & Related papers  (2025-11-10T04:48:48Z)
• CaRF: Enhancing Multi-View Consistency in Referring 3D Gaussian Splatting Segmentation [20.561664000265765]
  Referring 3D Gaussian Splatting (R3DGS) aims to interpret free-form language expressions and localize the corresponding 3D regions in Gaussian fields.<n>Existing pipelines still struggle with cross-modal alignment between language and 3D geometry.<n>We present Camera Aware Referring Field (CaRF), a framework that operates directly in the 3D Gaussian space and achieves multi view consistency.<n>This work promotes more reliable and view consistent 3D scene understanding, with potential benefits for embodied AI, AR/VR interaction, and autonomous perception.
  arXiv  Detail & Related papers  (2025-11-06T02:24:04Z)
• OpenInsGaussian: Open-vocabulary Instance Gaussian Segmentation with Context-aware Cross-view Fusion [89.98812408058336]
  We introduce textbfOpenInsGaussian, an textbfOpen-vocabulary textbfInstance textbfGaussian segmentation framework with Context-aware Cross-view Fusion.<n>OpenInsGaussian achieves state-of-the-art results in open-vocabulary 3D Gaussian segmentation, outperforming existing baselines by a large margin.
  arXiv  Detail & Related papers  (2025-10-21T03:24:12Z)
• Where, Not What: Compelling Video LLMs to Learn Geometric Causality for 3D-Grounding [0.8883733362171032]
  We propose a novel training framework called What-Where Representation Re-Forming (W2R2) to tackle this issue.<n>Our approach fundamentally reshapes the model's internal space by designating 2D features as semantic beacons for "What" identification and 3D features as spatial anchors for "Where" localization.<n>Experiments conducted on ScanRefer and ScanQA demonstrate the effectiveness of W2R2, with significant gains in localization accuracy and robustness.
  arXiv  Detail & Related papers  (2025-10-19T22:40:18Z)
• A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision [65.33043028101471]
  We present a novel framework for training 3D image-conditioned diffusion models using only 2D supervision.<n>Most existing 3D generative models rely on full 3D supervision, which is impractical due to the scarcity of large-scale 3D datasets.
  arXiv  Detail & Related papers  (2024-12-01T00:29:57Z)
• Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection [40.14197775884804]
  MonoASRH is a novel monocular 3D detection framework composed of Efficient Hybrid Feature Aggregation Module (EH-FAM) and Adaptive Scale-Aware 3D Regression Head (ASRH)<n>EH-FAM employs multi-head attention with a global receptive field to extract semantic features for small-scale objects.<n>ASRH encodes 2D bounding box dimensions and then fuses scale features with the semantic features aggregated by EH-FAM.
  arXiv  Detail & Related papers  (2024-11-05T02:33:25Z)
• GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision [49.839374549646884]
  This paper presents GEOcc, a Geometric-Enhanced Occupancy network tailored for vision-only surround-view perception.<n>Our approach achieves State-Of-The-Art performance on the Occ3D-nuScenes dataset with the least image resolution needed and the most weightless image backbone.
  arXiv  Detail & Related papers  (2024-05-17T07:31:20Z)
• S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR [50.435592120607815]
  Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR) Previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S2Former-OR.
  arXiv  Detail & Related papers  (2024-02-22T11:40:49Z)
• ORA3D: Overlap Region Aware Multi-view 3D Object Detection [11.58746596768273]
  Current multi-view 3D object detection methods often fail to detect objects in the overlap region properly. We propose using the following two main modules: (1) Stereo Disparity Estimation for Weak Depth Supervision and (2) Adrial Overlap Region Discriversaminator. Our proposed method outperforms current state-of-the-art models, i.e., DETR3D and BEVDet.
  arXiv  Detail & Related papers  (2022-07-02T15:28:44Z)
• Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding [61.57847727651068]
  Temporal sentence grounding aims to localize a target segment in an untrimmed video semantically according to a given sentence query. Most previous works focus on learning frame-level features of each whole frame in the entire video, and directly match them with the textual information. We propose a novel Motion- and Appearance-guided 3D Semantic Reasoning Network (MA3SRN), which incorporates optical-flow-guided motion-aware, detection-based appearance-aware, and 3D-aware object-level features.
  arXiv  Detail & Related papers  (2022-03-06T13:57:09Z)
• Cross-Modality 3D Object Detection [63.29935886648709]
  We present a novel two-stage multi-modal fusion network for 3D object detection. The whole architecture facilitates two-stage fusion. Our experiments on the KITTI dataset show that the proposed multi-stage fusion helps the network to learn better representations.
  arXiv  Detail & Related papers  (2020-08-16T11:01:20Z)

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.