UMAS3D: Unified Mask-Free 3D Editing with Region-Adaptive Source Preservation

Xianlin Zeng; Xiaolong Shen; Ruijie Quan; Yi Yang

cs.CVOct 11, 2026

UMAS3D: Unified Mask-Free 3D Editing with Region-Adaptive Source Preservation

Authors: Xianlin Zeng, Xiaolong Shen, Ruijie Quan, Yi Yang

Organizations: ReLER, College of Artificial Intelligence, Zhejiang University

Abstract

3D editing aims to modify an existing 3D asset according to user intent while preserving geometry and appearance outside the intended edit region. Existing methods often rely on task-specific training, source-3D inversion, or external 3D masks, and lack a unified framework for diverse editing conditions and tasks. We propose UMAS3D, a unified 3D editing framework that is training-free, inversion-free, and free from external 3D masks. UMAS3D converts text, bounding boxes, reference images, and their combinations into common target-view guidance, after which addition, deletion, modification, appearance, and pose editing share the same downstream 3D generation process. Our key insight is to couple online edit-region estimation with region-aware source preservation. Mask-Free Adaptive Region Modeling (MARM) initializes an Edit/Keep prior from source-target visual differences and progressively refines it as the target structure evolves, while Source-Guided Preservation Steering (SGPS) constrains the current Keep-region states to retain source geometry and appearance without suppressing target-driven changes. Experiments show that UMAS3D supports addition, deletion, modification, appearance, and pose editing while effectively preserving unedited source content. These advances make UMAS3D a unified framework for flexible and source-preserving 3D editing across diverse editing conditions.

Figures & tables

Figure 1: Overview of UMAS3D. A training-free framework that unifies multimodal conditions and five 3D editing tasks through adaptive region refinement and selective source preservation.

Figure 2: Core 3D editing pipeline of UMAS3D. MARM initializes and adaptively refines the Edit/Keep regions. SGPS preserves source-consistent content across structure and SLAT spaces.

Table 3

		Unedited-region Preservation			Overall Target Quality
Method	Venue	Pres-IoU $\uparrow$	Pres-PSNR $\uparrow$	Pres-SSIM $\uparrow$	DINO-I $\uparrow$	CD-L1 $\downarrow$	F@1% $\uparrow$	NC $\uparrow$
EditP23	arXiv’25	0.1745	7.8001	0.4363	0.5598	0.0612	0.3684	0.6905
Easy3E	CVPR’26	0.5643	17.0366	0.7149	0.8568	0.0190	0.7719	0.8502
VoxHammer	3DV’26	0.5691	16.1752	0.6056	0.8468	0.0186	0.7598	0.8521
Nano3D	ICLR’26	0.5580	16.0651	0.6176	0.8554	0.0182	0.7696	0.8540
Vinedresser3D	CVPR’26	0.6030	19.8795	0.7913	0.8797	0.0158	0.8031	0.8712

Table 3: Quantitative comparison with existing 3D editing methods. Best results are highlighted in bold with a light red background.

Figure 3: Qualitative comparison with existing 3D editing methods.

Method	Instruction Alignment $\uparrow$	Visual Quality $\uparrow$	Source Preservation $\uparrow$
VoxHammer	7%	7%	8%
Nano3D	7%	9%	8%
Vinedresser3D	8%	11%	9%
3DEditFormer	21%	19%	14%
UMAS3D	57%	54%	61%

Table 4: Human evaluation results. Each value denotes the percentage of times a method is selected as the preferred result under the corresponding criterion.

	Components				Unedited-region Preservation			Overall Target Quality
Model	Region Init.	S1 SGPS	S2 SGPS	Adapt.	Pres-IoU $\uparrow$	Pres-PSNR $\uparrow$	Pres-SSIM $\uparrow$	DINO-I $\uparrow$	CD-L1 $\downarrow$	F@1% $\uparrow$	NC $\uparrow$
A	–	–	–	–	0.1328	9.2586	0.4355	0.8644	0.0570	0.2861	0.6755
B	Ext.	$\checkmark$	–	–	0.5437	16.0835	0.2619	0.8521	0.0162	0.7949	0.8633
C	Ext.	$\checkmark$	$\checkmark$	–	0.6438	17.2126	0.6770	0.8780	0.0153	0.8001	0.8800
D	Ext.	$\checkmark$	$\checkmark$	$\checkmark$	0.6341	19.8069	0.6966	0.8798	0.0146	0.8048	0.8789
E	Mask-free	$\checkmark$	$\checkmark$	–	0.7094	18.6103	0.7737	0.8778	0.0152	0.8022	0.8959

Table 5: Quantitative ablation of UMAS3D components. Ext. : external 3D-mask initialization; Mask-free : MARM initialization without external 3D masks; S1/S2 SGPS : Stage 1/Stage 2 SGPS; Adapt. : adaptive Edit/Keep refinement. Best results are highlighted in bold with a light background.

Figure 4: Qualitative ablation of UMAS3D. Variants follow Table 5 . The first-row instruction is “Remove both bee wings.”, and the second-row instruction is “Replace the human arm structure with robotic mechanical arms.”

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Input	Text	Bounding Box	Reference Image
$T$	[Original Instruction] The original editing instruction is retained verbatim without rewriting.	–	–
$T+B$	[Modality-Specific Text] Since the Bounding Box provides where the edit occurs, redundant spatial descriptions already conveyed by the box are removed, while preserving the editing operation and target content or appearance. The original editing intent remains unchanged.	[Four Source Views] + [Original Instruction] Select the source view that best exposes the edit-relevant region, identify the source-side edit anchor , and return a tight 2D box $[x_{1},y_{1},x_{2},y_{2}]$ normalized to $[0,1000]$ . For Addition, the box localizes the source-side attachment or placement region rather than future target content absent from the source.	–
$T+R$	[Modality-Specific Text] Since the Reference Image provides what / appearance , the text retains the editing operation, spatial location, and necessary relation while removing target visual details already represented by the reference.	–	[Selected Source View] + [Original Instruction] Generate a standalone visual reference that represents only the target visual information required by the edit. For Addition, the reference depicts the object to be added; for Modification, the desired replacement or modified part; and for Appearance, the target color, material, texture, or pattern.
$B+R$	–	Uses the same source-side Bounding Box as $T+B$ .	Uses the same Reference Image as $T+R$ .
$T+B+R$	[Modality-Specific Text] The Bounding Box provides where , while the Reference Image provides what / appearance . The text therefore retains only the editing operation and any relation still required to specify the intended edit.	Uses the same source-side Bounding Box as $T+B$ .	Uses the same Reference Image as $T+R$ .

Appendix

Table 6: Construction of the five multimodal editing conditions used in our benchmark. Bracketed terms denote sample-specific content.

Panel of Figure 5 — Figure 5: Examples of the constructed multimodal editing conditions. Each dashed box shows the Bounding Box visualization, the generated Reference Image, and the original textual editing instruction. Modality-specific text variants are constructed according to Table 6 .

Input	Gemini 3 Pro Image Input	Prompt Construction and Modality Roles
$T$	[Source View]	Text is the only explicit editing condition. The textual instruction defines the requested operation and target semantics, while the spatial location is inferred jointly from the instruction and the source image. The prompt explicitly prohibits introducing content that is not requested by the instruction.
$T+B$	[Source View] + [BBox Visualization]	Text defines what operation should be performed, whereas the Bounding Box provides only a soft cue for where the relevant source region is located. The box is not treated as a hard mask, exact boundary, crop, or target silhouette, and the actual edited area may extend slightly beyond or remain within the box when required for coherent geometry and appearance. The BBox itself must not appear in the generated image.
$T+R$	[Source View] + [Reference Image]	Text is authoritative for the editing operation, while the Reference Image provides the requested visual target information. Depending on the editing intent inferred from the available conditions, the reference may provide target identity, shape, parts, color, material, or texture. Its framing, pose, background, shadows, and unrelated content are not directly copied; instead, the relevant visual attributes are adapted to the source image.
$B+R$	[Source View] + [BBox Visualization] + [Reference Image]	No user-provided Text condition, original textual instruction, or edit-type label is supplied. The Bounding Box provides approximate where information, while the Reference Image provides what / appearance information. The editing operation is inferred from the source image, the localized source region, and the visual relationship between the source and the reference, and is represented internally by the unified editing description $E$ .
$T+B+R$	[Source View] + [BBox Visualization] + [Reference Image]	All three modalities jointly constrain the edit. Text is authoritative for the requested operation / semantics , the Bounding Box provides soft where guidance, and the Reference Image provides the desired what / appearance . The spatial and visual conditions complement the textual instruction without overriding its editing intent.

Appendix

Table 7: Prompt construction for target-view generation under the five multimodal input combinations. The five settings share the same generation pipeline but differ in the roles assigned to Text, Bounding Box, and Reference Image.

Panel of Figure 6 — Figure 6: Examples of target-view generation. Each dashed box groups a selected source view and its corresponding target view generated by Gemini 3 Pro Image. The examples cover diverse objects and editing operations.

Figure 7: Unified 3D editing across multimodal instruction combinations and editing tasks. UMAS3D converts heterogeneous multimodal conditions into target-view guidance and handles Addition, Deletion, Modification, Appearance and Pose through the same 3D editing pipeline.

Panel of Figure 8 — Figure 8: Visualization of MARM initialization and adaptive region refinement. Each row shows the editing instruction, selected source view, generated target view, coarse 3D region prior, initial Edit/Keep partition $\mathbf{M}_{0}$ , and final partition $\mathbf{M}_{T}$ after Stage 1 refinement. Differences between $\mathbf{M}_{0}$ and $\mathbf{M}_{T}$ directly visualize the online reassignment of Edit/Keep regions within the single Stage 1 trajectory.

Component	Parameter	Value
TRELLIS	Stage 1 / Stage 2 steps	$100/25$
MARM	Adaptive regions $K$	$8$
	Provisional macro regions	$16$
	EMA coefficient $\lambda$	$0.8$
	Initial warm-up	first $35$ Stage 1 steps
	Region update interval	every $5$ steps

Appendix

Table 8: Main inference parameters of UMAS3D.

Panel of Figure 9 — Figure 9: Additional qualitative 3D editing results of UMAS3D. Each dashed box presents four views of the source 3D and four corresponding views of the edited 3D, separated by a vertical dashed line.

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