NOFACE3D: NOise-scheduled Focused Anchoring and Controlled Enhancement for 3D Generation

James Rose; Jingwen Liang; Wenchao Ma; Oren Jacob; Haomiao Jiang

cs.CVOct 9, 2026

NOFACE3D: NOise-scheduled Focused Anchoring and Controlled Enhancement for 3D Generation

Authors: James Rose, Jingwen Liang, Wenchao Ma, Oren Jacob, Haomiao Jiang

Organizations: Roblox · The Pennsylvania State University

Abstract

Recent image-to-3D models generate complete, high-quality assets from a single image, with global shape and appearance that are already convincing; what remains is the fine scale, where small, thin, elongated, or detail-rich regions are reconstructed softly and under-resolved. This gap can arise in generators with fixed-resolution spatial grids: the same spatial sampling density is used for a large, smooth, low-curvature surface and for a thin, high-curvature structure that carries most of the perceived detail. To this end, we introduce NOFACE3D, a training-free framework that refines these detail-critical regions while preserving the rest of the asset. Given a generated mesh, its input image, and a refinement box, NOFACE3D extracts the selected submesh, scales it up to fill the generator's input volume, and re-encodes it into the full-resolution latent grid. Conditioned on the image crop, it refines the region through flow-based sampling with a spatially varying noise schedule that anchors boundary latents. The refined region is then integrated back into the original asset, preserving the surrounding geometry and appearance. We implement NOFACE3D on TRELLIS.2 and its sparse voxel representation; all of it operates purely at inference time on the frozen pretrained generator. We evaluate NOFACE3D on PAniC-3D, TexVerse and BigDetail, a new benchmark of 339 assets with annotated detail-critical regions. On all three, it generates sharper geometry and finer texture detail than the baselines. Project page: https://idkcouscous.github.io/NoFACE3D/

Figures & tables

Figure 2: Overview of NOFACE3D . Given an asset generated by an off-the-shelf image-to-3D model and a user-marked region, we scale up the region to fill the generator’s input volume and re-encode it into the full-resolution latent grid, assign each token a release level $\tau$ from a boundary-distance field (anchored near attachment boundaries, with stronger regeneration farther away), and resample geometry and texture with spatially varying SDEdit ( Meng et al., 2022 ) , re-anchoring unreleased tokens to the base asset at every step. The refined mesh replaces the base faces inside the crop box.

	Geometry					Appearance					GPT-5.6
	CD	F1 0.25%	F1 1%	IoU	SNE	PSNR	SSIM	LPIPS	DINO	FID	Ovr.	Geo.
Method	$\downarrow$	$\uparrow$	$\uparrow$	$\uparrow$	$\downarrow$	$\uparrow$	$\uparrow$	$\downarrow$	$\uparrow$	$\downarrow$	$\uparrow$	$\uparrow$
PAniC-3D ( $N{=}329$ )
GT	reference										$8.52$	$6.83$
TRELLIS.2	$\mathbf{2.938}$	$0.138$	$0.389$	$0.383$	$1.12$	$15.80$	$0.882$	$0.118$	$0.792$	$43.17$	$6.48$	$6.82$
Easy3E	$3.955$	$0.074$	$0.265$	$0.294$	$1.24$	$13.17$	$0.878$	$0.128$	$0.588$	$98.74$	$2.44$	$3.02$

Table 1: Comparison with refinement and editing methods. Whole-asset metrics against GT; CD and SNE

{\times}100

; IoU at

128^{3}

; FID is set-level. GPT-5.6: pairwise win share (1–10, chance

5.0

) for overall quality and geometry detail.

Figure 3: Qualitative comparison on PAniC-3D (top) and TexVerse (bottom): texture and normal map per method (DetailGen3D is untextured).

Table 4

Figure 4: Qualitative results on BigDetail . Top: input image with the labeled region and its enlargement. Below: TRELLIS.2 and NOFACE3D , texture and normal map.

Figure 5: Noise scheduling ablation. Top: regeneration without anchoring or noise scheduling. Bottom: with noise scheduling. Texture and normal views use matched cameras and detail crops. Orange boxes mark the regions enlarged below each full-asset view.

Figure 6: Localized 3D editing. A Genshin character gains a hat (head region), then robotic legs (leg region). Dashed boxes mark the regions.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Metric	TRELLIS.2	NOFACE3D
Chamfer $\downarrow$	$\mathbf{3.359\pm 2.764}$	$3.370\pm 2.773$
F1 @ $0.25\%$ $\uparrow$	$0.141\pm 0.070$	$\mathbf{0.144\pm 0.070}$
F1 @ $1\%$ $\uparrow$	$0.37219\pm 0.15536$	$0.37222\pm 0.15548$
Surface IoU $128^{3}$ $\uparrow$	$0.373\pm 0.140$	$\mathbf{0.381\pm 0.142}$
SNE $\downarrow$	$\mathbf{1.312\pm 0.610}$	$1.314\pm 0.620$
PSNR $\uparrow$	$15.70\pm 2.17$	$\mathbf{16.53\pm 2.76}$

Appendix

Table 4: TRELLIS.2 versus NOFACE3D on

4{,}892

PAniC-3D assets against artist GT. Mean

\pm

population s.d.; Chamfer and SNE

{\times}100

; FID is set-level.

	GPT-4o			GPT-5.6
Criterion	GT	TRELLIS.2	NOFACE3D	GT	TRELLIS.2	NOFACE3D
Fidelity / completeness	$5.68$	$4.41$	$\mathbf{4.91}$	$6.38$	$3.61$	$\mathbf{5.01}$
3D plausibility	$5.63$	$4.64$	$\mathbf{4.73}$	$5.87$	$4.33$	$\mathbf{4.80}$
Geo.–tex. alignment	$5.74$	$4.44$	$\mathbf{4.82}$	$4.94$	$4.67$	$\mathbf{5.40}$
Texture details	$6.05$	$4.08$	$\mathbf{4.86}$	$7.46$	$2.42$	$\mathbf{5.12}$
Geometry details	$5.64$	$4.63$	$\mathbf{4.73}$	$5.29$	$4.75$	$\mathbf{4.96}$

Appendix

Table 5: Monadic judged comparison on

4{,}892

PAniC-3D assets: each mesh is rated alone, and win shares (1–10) are derived from the ratings.

Metric	TRELLIS.2	Easy3E	Elevate3D	DetailGen3D	NOFACE3D
Chamfer $\downarrow$	$\mathbf{2.938\pm 1.884}$	$3.955\pm 2.071$	$3.059\pm 1.896$	$2.939\pm 1.900$	$2.939\pm 1.878$
F1 @ $0.25\%$ $\uparrow$	$0.138\pm 0.073$	$0.074\pm 0.034$	$0.115\pm 0.051$	$0.127\pm 0.056$	$\mathbf{0.142\pm 0.074}$
F1 @ $1\%$ $\uparrow$	$0.389\pm 0.166$	$0.265\pm 0.111$	$0.372\pm 0.146$	$\mathbf{0.393\pm 0.153}$	$0.389\pm 0.165$
Surface IoU $128^{3}$ $\uparrow$	$0.383\pm 0.144$	$0.294\pm 0.113$	$0.377\pm 0.135$	$0.386\pm 0.141$	$\mathbf{0.391\pm 0.147}$
SNE $\downarrow$	$1.12\pm 0.40$	$1.24\pm 0.37$	$1.26\pm 0.38$	$\mathbf{1.04\pm 0.40}$	$1.12\pm 0.40$
PSNR $\uparrow$	$15.80\pm 1.90$	$13.17\pm 1.08$	$16.59\pm 1.76$	–	$\mathbf{16.78\pm 2.44}$

Appendix

Table 6: Objective comparison on PAniC-3D (

N{=}329

). Mean

\pm

s.d.; Chamfer and SNE

{\times}100

; FID is set-level.

Metric	TRELLIS.2	Easy3E	Elevate3D	DetailGen3D	NOFACE3D
Chamfer $\downarrow$	$7.27\pm 14.9$	$11.7\pm 19.3$	$13.7\pm 18.7$	$35.4\pm 31.0$	$\mathbf{7.22\pm 14.8}$
F1 @ $0.25\%$ $\uparrow$	$0.123\pm 0.147$	$0.060\pm 0.059$	$0.058\pm 0.064$	$0.009\pm 0.010$	$\mathbf{0.140\pm 0.150}$
F1 @ $1\%$ $\uparrow$	$0.306\pm 0.230$	$0.219\pm 0.188$	$0.194\pm 0.178$	$0.032\pm 0.031$	$\mathbf{0.320\pm 0.234}$
Surface IoU $128^{3}$ $\uparrow$	$0.334\pm 0.247$	$0.250\pm 0.193$	$0.198\pm 0.169$	$0.055\pm 0.044$	$\mathbf{0.337\pm 0.244}$
SNE $\downarrow$	$1.97\pm 1.33$	$1.81\pm 1.37$	$2.16\pm 1.40$	$\mathbf{1.23\pm 0.84}$	$1.61\pm 1.18$
PSNR $\uparrow$	$18.02\pm 9.24$	$17.25\pm 9.10$	$16.97\pm 9.15$	–	$\mathbf{19.57\pm 9.29}$

Appendix

Table 7: Objective comparison on TexVerse (

N{=}151

). Mean

\pm

s.d.; Chamfer and SNE

{\times}100

Criterion	GT	TRELLIS.2	Easy3E	Elevate3D	DetailGen3D	NOFACE3D
GPT-4o
Fidelity / completeness	$7.84$	$6.41$	$2.17$	$4.73$	–	$\mathbf{7.57}$
3D plausibility	$7.17$	$5.77$	$2.76$	$2.96$	$4.54$	$\mathbf{6.81}$
Geo.–tex. alignment	$7.90$	$6.45$	$2.41$	$4.39$	–	$\mathbf{7.60}$
Texture details	$7.87$	$6.46$	$1.95$	$4.93$	–	$\mathbf{7.61}$
Geometry details	$7.36$	$5.88$	$2.23$	$3.04$	$4.50$	$\mathbf{6.98}$

Appendix

Table 8: Pairwise judged comparison on PAniC-3D (

N{=}329

). Win share (1–10, chance

5.0

Criterion	GT	TRELLIS.2	Easy3E	Elevate3D	DetailGen3D	NOFACE3D
GPT-4o
Fidelity / completeness	$5.58$	$5.24$	$5.34$	$5.07$	–	$\mathbf{5.72}$
3D plausibility	$5.61$	$5.21$	$5.41$	$5.01$	$3.12$	$\mathbf{5.64}$
Geo.–tex. alignment	$5.59$	$5.23$	$5.38$	$5.05$	–	$\mathbf{5.66}$
Texture details	$5.62$	$5.25$	$5.34$	$5.03$	–	$\mathbf{5.71}$
Geometry details	$5.58$	$5.22$	$5.39$	$5.04$	$3.12$	$\mathbf{5.65}$

Appendix

Table 9: Pairwise judged comparison on TexVerse (

N{=}151

). Win share (1–10, chance

5.0

	GPT-4o		GPT-5.6
Criterion	TRELLIS.2	NOFACE3D	TRELLIS.2	NOFACE3D
Fidelity / completeness	$4.40$	$\mathbf{5.60}$	$3.76$	$\mathbf{6.24}$
3D plausibility	$4.42$	$\mathbf{5.58}$	$4.60$	$\mathbf{5.40}$
Geo.–tex. alignment	$4.40$	$\mathbf{5.60}$	$3.89$	$\mathbf{6.11}$
Texture details	$4.57$	$\mathbf{5.43}$	$4.61$	$\mathbf{5.39}$
Geometry details	$4.60$	$\mathbf{5.40}$	$4.21$	$\mathbf{5.79}$

Appendix

Table 10: Pairwise judged comparison on BigDetail (

N{=}339

), TRELLIS.2 versus NOFACE3D . Head-to-head win share (1–10); each pair sums to

10

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Recent native 3D generators achieve high-quality asset creation by modeling structured 3D latents with flow-based transformers, but their high-resolution generation remains computationally expensive due to costly iterative sampling. In this work, we introduce a few-step distillation framework for native 3D generation, enabled by continuous self-consistency training with a custom JVP FlashAttention kernel and an analytic velocity regression objective for sparse structured 3D latents. To stabilize joint optimization, we further introduce an adaptive loss weighting strategy that automatically balances them without manual tuning. Beyond acceleration, we reveal a previously overlooked failure mode of few-step native 3D generation: sampling-induced latent distribution drift preserves geometric correctness but disrupts downstream PBR texture synthesis, causing texture artifacts on geometrically valid assets. To address this issue, we propose Channel Match, a training-free adaptive per-channel latent calibration method that restores the latent distribution without modifying either generator. We validate our approach on TRELLIS.2 at the

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