Intrinsic Geometry-Appearance Consistency Optimization for Sparse-View Gaussian Splatting

Kaiqiang Xiong; Rui Peng; Jiahao Wu; Zhanke Wang; Jie Liang; Xiaoyun Zheng; Feng Gao; Ronggang Wang

cs.CVMar 3, 2026

Intrinsic Geometry-Appearance Consistency Optimization for Sparse-View Gaussian Splatting

Authors: Kaiqiang Xiong, Rui Peng, Jiahao Wu, Zhanke Wang, Jie Liang, Xiaoyun Zheng, Feng Gao, Ronggang Wang

Organizations: Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University · Peng Cheng Laboratory · Alibaba Group · School of Arts, Peking University ·

Abstract

3D human reconstruction from a single image is a challenging problem and has been exclusively studied in the literature. Recently, some methods have resorted to diffusion models for guidance, optimizing a 3D representation via Score Distillation Sampling(SDS) or generating a back-view image for facilitating reconstruction. However, these methods tend to produce unsatisfactory artifacts (e.g. flattened human structure or over-smoothing results caused by inconsistent priors from multiple views) and struggle with real-world generalization in the wild. In this work, we present MVD-HuGaS, enabling free-view 3D human rendering from a single image via a multi-view human diffusion model. We first generate multi-view images from the single reference image with an enhanced multi-view diffusion model, which is well fine-tuned on high-quality 3D human datasets to incorporate 3D geometry priors and human structure priors. To infer accurate camera poses from the sparse generated multi-view images for reconstruction, an alignment module is introduced to facilitate joint optimization of 3D Gaussians and camera poses. Furthermore, we propose a depth-based Facial Distortion Mitigation module to refine the generated facial regions, thereby improving the overall fidelity of the reconstruction. Finally, leveraging the refined multi-view images, along with their accurate camera poses, MVD-HuGaS optimizes the 3D Gaussians of the target human for high-fidelity free-view renderings. Extensive experiments on Thuman2.0 and 2K2K datasets show that the proposed MVD-HuGaS achieves state-of-the-art performance on single-view 3D human rendering.

Figures & tables

Figure 2: Framework of ICO-GS. Given sparse input views, we initialize 3D Gaussians and extract deep features. Our method enforces intrinsic geometry-appearance consistency through two synergistic components: (1) Robust Geometric Regularization. Source features are warped to reference views via rendered depth, establishing occlusion-aware multi-view constraints through: (a) Robust Multi-view Photometric Consistency that employs pixel-wise top- $k$ selection for occlusion handling, and (b) Edge-aware Depth Smoothness that preserves sharp geometric structures. (2) Geometry-Guided Appearance Optimization. We leverage geometrically reliable regions identified by (c) Cycle Consistency Depth Filtering to synthesize virtual views, then apply (d) Virtual-view Photometric Consistency between synthesized and rendered images to propagate geometric correctness into appearance learning.

Figure 3: Geometry-appearance discrepancy under sparse-view settings. From top to bottom: RGB on training views, depth on training views, and RGB on test views, rendered by 3D Gaussian Splatting [ 19 ] with varying training view densities. With decreasing views, training-view appearance (top) remains well-fitted, but depth quality (middle) collapses with noise and floaters due to insufficient multi-view constraints. This geometry-appearance discrepancy leads to severe artifacts in novel-view rendering (bottom).

Methods	PSNR↑			SSIM↑			LPIPS↓
Methods	3-view	6-view	9-view	3-view	6-view	9-view	3-view	6-view	9-view
DietNeRF [ 16 ]	14.94	21.75	24.28	0.370	0.717	0.801	0.496	0.248	0.183
RegNeRF [ 29 ]	19.08	23.10	24.86	0.587	0.760	0.820	0.336	0.206	0.161
FreeNeRF [ 42 ]	19.63	23.73	25.13	0.612	0.779	0.827	0.308	0.195	0.160
SparseNeRF [ 35 ]	19.86	23.26	24.27	0.714	0.741	0.781	0.243	0.235	0.228
3DGS [ 19 ]	15.52	19.45	21.13	0.405	0.627	0.715	0.408	0.268	0.214

Table 1: Quantitative comparisons on LLFF [ 26 ] dataset under sparse view settings.

Figure 4: Visual comparison on LLFF [ 26 ] dataset.

Methods	PSNR↑			SSIM↑			LPIPS↓
Methods	3-view	6-view	9-view	3-view	6-view	9-view	3-view	6-view	9-view
DietNeRF [ 16 ]	11.85	20.63	23.83	0.633	0.778	0.823	0.214	0.201	0.173
RegNeRF [ 29 ]	18.89	22.20	24.93	0.745	0.841	0.884	0.190	0.117	0.089
FreeNeRF [ 42 ]	19.52	23.25	25.38	0.787	0.844	0.888	0.173	0.131	0.102
SparseNeRF [ 35 ]	19.47	-	-	0.829	-	-	0.183	-	-
3DGS [ 19 ]	10.99	20.33	22.90	0.585	0.776	0.816	0.313	0.223	0.173

Table 2: Quantitative comparisons on DTU dataset under sparse view settings.

Figure 5: Visual comparison on DTU [ 18 ] dataset.

Methods	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
DietNeRF [ 16 ]	22.50	0.823	0.124
RegNeRF [ 29 ]	23.86	0.852	0.105
FreeNeRF [ 42 ]	24.26	0.883	0.098
SparseNeRF [ 35 ]	22.41	0.861	0.199
3DGS [ 19 ]	22.23	0.858	0.114
FSGS [ 47 ]	22.76	0.829	0.157

Table 3: Quantitative comparison on Blender [ 27 ] for 8 views.

Figure 6: Visual comparison on Blender [ 27 ] dataset.

$\mathcal{L}_{\text{mpc}}^{\text{Fea}}$	$\mathcal{L}_{\text{smooth}}$	CCDF	$\mathcal{L}_{\text{app}}$	LLFF (3-view)			DTU (3-view)
$\mathcal{L}_{\text{mpc}}^{\text{Fea}}$	$\mathcal{L}_{\text{smooth}}$	CCDF	$\mathcal{L}_{\text{app}}$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
$\times$	$\times$	$\times$	$\times$	21.44	0.751	0.168	20.71	0.862	0.111
$\times$	✓	✓	✓	21.82	0.761	0.165	21.31	0.883	0.099
✓	$\times$	✓	✓	22.16	0.775	0.159	21.67	0.879	0.093
✓	✓	$\times$	✓	21.86	0.767	0.162	21.25	0.875	0.104
✓	✓	✓	$\times$	21.79	0.763	0.164	21.20	0.870	0.105

Table 4: Ablation studies on LLFF [ 26 ] and DTU [ 18 ] .

Figure 7: Visualization of ablation study results using 3-views.

Multi-View Diffusion Models

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Apr 28, 2026

Generalizable Human Gaussian Splatting via Multi-view Semantic Consistency

Recently, generalizable human Gaussian splatting from sparse-view inputs has been actively studied for the photorealistic human rendering. Most existing methods rely on explicit geometric constraints or predefined structural representations to accurately position 3D Gaussians. Although these approaches have shown the remarkable progress in this field, they still suffer from inconsistent feature representations across multi-view inputs due to complex articulations of the human body and limited overlaps between different views. To address this problem, we propose a novel method to accurately localize 3D Gaussians and ultimately improve the quality of human rendering. The key idea is to unproject latent embeddings encoded from each viewpoint into a shared 3D space through predicted depth maps and recalibrate them belonging to the same body part based on cross-view attention. This helps the model resolve the spatial ambiguity occurring in highly textured regions as well as occluded body parts, thus leading to the accurate localization of 3D Gaussians. Experimental results on benchmark datasets show that the proposed method efficiently improves the performance of generalizable human Gaussian splatting from sparse-view inputs.

Jingi Kim, Wonjun Kim

Konkuk University

Multi-View Consistency 3D Gaussian Splatting Cross-View Attention Sparse-View 3D Reconstruction Gaussian Splatting

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Sep 19, 2026

D3GS: Depth, DINO, and RGB Diffusion Co-Guided 3D Gaussian Splatting for Sparse-View Reconstruction

Novel view synthesis from sparse inputs remains challenging for 3D Gaussian Splatting (3DGS) due to ambiguous geometry, cross-view inconsistency, and missing details in under-constrained regions, resulting in degraded reconstruction and unstable rendering. To tackle these issues, we propose D

^{3}

GS, a Depth-DINO-Diffusion guided sparse-view Gaussian reconstruction framework that jointly enhances geometry and appearance. D

^{3}

GS first recovers a high-resolution, metric depth map via diffusion-based completion and DPT (Dense Prediction Transformer) refinement, providing robust Gaussian initialization and geometric constraints. Then, a DINO-guided view-consistent learning is introduced to augment Gaussian attributes with structural features, improving multi-view consistency. Finally, a diffusion-based Gaussian refinement module injects generative priors into an iterative optimization strategy, enhancing high-frequency geometric and appearance details within the Gaussian representation. Experiments on DTU, LLFF, and Mip-NeRF 360 show that D

^{3}

GS achieves consistent and substantial improvements over strong baselines, with ablation studies validating the effectiveness and complementary roles of each component.

Yunqi Gao, Zhanfeng Liao, Hanzhang Tu +7

Central China Normal University · Tsinghua University · Fuzhou University +4

Sparse-View 3D Reconstruction 3D Gaussian Splatting 3D Reconstruction Multi-View Consistency

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May 12, 2026

GeoQuery: Geometry-Query Diffusion for Sparse-View Reconstruction

3D Gaussian Splatting (3DGS) has emerged as a prominent paradigm for 3D reconstruction and novel view synthesis. However, it remains vulnerable to severe artifacts when trained under sparse-view constraints. While recent methods attempt to rectify artifacts in rendered views using image diffusion models, they typically rely on multi-view self-attention to retrieve information from reference images. We observe that this mechanism often fails when the rendered novel views output by 3DGS are heavily corrupted: damaged query features lead to erroneous cross-view retrieval, resulting in inconsistent rendering refinement. To address this, we propose GeoQuery, a geometry-guided diffusion framework that integrates generative priors with explicit geometric cues via a novel Geometry-guided Cross-view Attention (GCA) mechanism. First, by leveraging predicted depth maps and camera poses, we construct a geometry-induced correspondence field to sample reference features, forming a geometry-aligned proxy query that replaces the corrupted rendering features. Furthermore, we design a new cross-view feature aggregation pipeline, in which we restrict the cross-view attention to a local window around each proxy query to effectively retrieve useful features while suppressing spurious matches. GeoQuery can be seamlessly integrated into existing diffusion-based pipelines, enabling robust reconstruction even under extreme view sparsity. Extensive experiments on sparse-view novel view synthesis and rendering artifact removal demonstrate the effectiveness of our approach.

Xiao Cao, Yuze Li, Youmin Zhang +4

University of Electronic Science and Technology of China & Rawmantic AI, China · Rawmantic AI, China · Tianjin University, China +1

3D Gaussian Splatting Diffusion Models for Image Restoration Novel View Synthesis Sparse-View 3D Reconstruction

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Abstract

Multi-View Diffusion Models

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