UltraTex: Unleashing 2K Multi-View Diffusion for 3D Texturing

Yibo Zhang; Ze Yuan; Nan Cao; Li Zhang; Yan-Pei Cao; Yuan-Chen Guo; Rui Ma

cs.CVSep 19, 2026

UltraTex: Unleashing 2K Multi-View Diffusion for 3D Texturing

Authors: Yibo Zhang, Ze Yuan, Nan Cao, Li Zhang, Yan-Pei Cao, Yuan-Chen Guo, Rui Ma

Organizations: Jilin University, Changchun, China · Shanghai Innovation Institute, Shanghai, China · The University of Hong Kong, Hong Kong, China · Tongji University, Shanghai, China ·

Abstract

High-quality texture generation is essential for creating realistic and production-ready 3D assets. Recent multi-view diffusion methods have shown promising results for image-guided 3D texturing, but they are typically constrained to low operating resolutions such as 512 or 768, making it difficult to preserve high-frequency details from high-resolution reference images. Scaling this paradigm to 2048 resolution is computationally prohibitive, as the unified multi-view sequence exceeds 212K tokens and incurs excessive memory and latency. In this paper, we present UltraTex, an efficient end-to-end framework for high-resolution multi-view diffusion-based 3D texturing. Our key observation is that object-centric multi-view renderings contain two major sources of redundancy: background-induced sequence redundancy and sparse token interactions within the foreground. To address them, we introduce Background Token Dropping, which removes background tokens before the DiT backbone, and Block-Sparse Attention, which reduces attention computation over the retained foreground sequence. To enable efficient foreground-only inference while avoiding reconstruction artifacts, we further design Foreground-Aware VAE Decoding to ensure the quality of the final high-resolution views. To satisfy the demanding data requirements of 2K-resolution multi-view diffusion training, we construct G-buffer TexVerse, a large-scale, ultra-high-resolution multi-view rendering dataset covering over 268,000 3D assets. Extensive experiments show that UltraTex generates visually faithful textures with rich fine-grained details, while substantially improving efficiency, achieving $20.6\times$ -- $91.1\times$ training speedup and $22.3\times$ -- $74.6\times$ end-to-end inference speedup over the baseline on common samples in our dataset. Code and data is at https://yiboz2001.github.io/UltraTex.

Figures & tables

Figure 1. A gallery of diverse 3D assets textured by UltraTex, showcasing high-fidelity results with rich fine-grained details.

Figure 2. Overview of UltraTex. UltraTex generates geometry-aligned 2048-resolution multi-view texture images for 3D texturing. UltraTex first removes all background tokens before the DiT blocks, and then applies block-sparse attention on the remaining foreground sequence. During inference, foreground-only denoising and foreground-aware VAE decoding generate multi-view results, which are reprojected onto the input mesh to obtain the final textured 3D asset.

Figure 3. Examples of foreground sparsity in object-centric multi-view renderings. Each image shows a six-view layout, where only the object foreground corresponds to surface regions requiring texture generation. The two examples contain only $24.9\%$ and $7.9\%$ foreground pixels, respectively, highlighting the substantial background redundancy.

Figure 4. Preliminary token-dropping experiment with pretrained FLUX. We remove background tokens according to the foreground masks in the multi-view layout while preserving the original positional indices of the retained tokens. The model can still denoise the resulting discontinuous partial sequence and produce spatially plausible foreground results.

Figure 5. Attention sparsity over the retained foreground sequence after Background Token Dropping. We visualize attention maps from a lower-resolution full-attention model trained with Background Token Dropping. Both Double-Stream (DS) and Single-Stream (SS) DiT block attention maps exhibit sparse patterns over the retained foreground tokens.

	Unshaded					Shaded					Relighting
Method	FID $\downarrow$	CLIP-FID $\downarrow$	CMMD $\downarrow$	CLIP-I $\uparrow$	LPIPS $\downarrow$	FID $\downarrow$	CLIP-FID $\downarrow$	CMMD $\downarrow$	CLIP-I $\uparrow$	LPIPS $\downarrow$	FID $\downarrow$	CLIP-FID $\downarrow$	CMMD $\downarrow$	CLIP-I $\uparrow$	LPIPS $\downarrow$
Step1X-3D	–	–	–	–	–	160.3	19.265	0.613	0.900	0.154	160.7	19.189	0.600	0.901	0.154
UniTEX	–	–	–	–	–	133.8	16.750	0.524	0.918	0.116	133.4	15.697	0.458	0.923	0.113
Hunyuan3D 2.1	153.48	18.995	1.150	0.908	0.136	123.1	14.354	0.432	0.929	0.119	126.0	14.117	0.409	0.930	0.117
LumiTex	147.31	18.251	1.223	0.914	0.138	110.6	12.265	0.351	0.939	0.107	114.5	11.982	0.316	0.941	0.105
UltraTex (Ours)	125.13	15.180	0.810	0.929	0.082	97.6	12.239	0.368	0.941	0.064	96.4	11.583	0.333	0.944	0.062

Table 1. Quantitative comparison with baseline methods.

\downarrow

indicates lower is better, and

\uparrow

indicates higher is better.

Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$
FLUX VAE (Full Image)	47.65	0.9937	0.0023
FLUX VAE (Noise Background)	29.66	0.9555	0.0263
Ours (w/o finetune)	37.81	0.9818	0.0084
Ours (w/ finetune)	50.26	0.9956	0.0038

Table 2. Quantitative comparison of different VAE reconstruction settings.

Figure 6. Qualitative comparison with state-of-the-art PBR-based methods (* denotes a commercial model). Our method consistently recovers finer texture details and achieves high visual quality across diverse inputs, including artist-created assets (first four examples) and in-the-wild images (last three examples).

Panel of Figure 7 — Figure 7. Efficiency analysis of UltraTex at 2048 resolution. Background Token Dropping (BTD) reduces training/inference cost and peak GPU memory by dropping background tokens and shortening the effective sequence, while Block-Sparse Attention further improves efficiency over the retained foreground tokens. With the adopted retention ratio $\rho=20\%$ , the combined design achieves $20.6\times$ – $91.1\times$ training speedup and $22.3\times$ – $74.6\times$ end-to-end inference speedup within the common foreground-ratio range of our dataset.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Figure S1 . Examples of albedo entropy filtering.

Figure S2 . Foreground ratio statistics of our rendered dataset. Most objects have relatively small foreground regions, with the distribution concentrated in the $5\%$ – $30\%$ range and only a few samples exceeding $40\%$ .

Figure S3 . Influence of the pretrained foundation model on identity preservation. From left to right: the reference image, the result using FLUX, and the result using FLUX.2-dev. FLUX.2-dev more faithfully preserves the distinctive posters, illustrations, and table layout of the reference. Three images of a bulletin board. The left image is the reference, the middle image is the FLUX result, and the right image is the FLUX.2-dev result. The right image retains the reference's poster designs and central table more faithfully than the middle image.

Figure S4 . PBR material generation with FLUX.2-dev as the pretrained foundation model. The reference image (left) conditions the generation of six-view roughness–metallic maps (right), visualized as packed material channels. A reference image of an armored character on the left, vertically centered next to a two-row, three-column grid of generated roughness and metallic maps on the right.

Table S1 . Efficiency analysis of UltraTex at 2048 resolution under different foreground ratios.

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