SREdit: Scene Response Guided Appearance Editing for Projection Mapping

Yuqiao Xia; Yuchen Deng; Ziheng Chang; Bingyao Huang

cs.CVOct 23, 2026

SREdit: Scene Response Guided Appearance Editing for Projection Mapping

Authors: Yuqiao Xia, Yuchen Deng, Ziheng Chang, Bingyao Huang

Organizations: College of Computer and Information Science, Southwest University, Chongqing, China

Abstract

Projection-based appearance editing changes selected visual attributes of a physical object through projection mapping (PM). To display a desired appearance, PM typically uses projector compensation, which computes a projector input that counteracts the geometric and photometric distortions of the scene. Compensation can only approximate this appearance within the available projection range, and it cannot revise the appearance itself. Pretrained LLM-based image editors provide a direct way to produce the desired appearance from a text prompt or reference image; projector compensation can then attempt to reproduce the edited result through projection. However, these editors do not have an accurate measure of the scene geometry or scene response. Therefore, their outputs may contain geometric misalignment, inconsistent shading, or changes outside the available projection range, which downstream projector compensation cannot correct. To address this problem, we introduce SREdit, an efficient response-guided framework for projection-based appearance editing. SREdit estimates a scene response map from the reconstructed scene that reflects how strongly projected illumination can affect different surface regions. SREdit uses the source appearance to constrain the early sampling process and preserve registration with the fixed physical object, while the scene response map adapts the amount of editing across surface regions. Experiments across two pretrained image editors show that SREdit improves projected appearance quality and reproduction fidelity. It adds less than one second per image with negligible VRAM overhead. Participants also consistently preferred the results produced with SREdit in our user study.

Figures & tables

Figure 1: Method overview. The pretrained image editor does not have an accurate measure of the scene response of the physical setup. During the first $r$ portion of sampling, SREdit uses the source appearance to preserve registration with the physical object, while the scene response map adapts the amount of editing across surface regions. Regions with lower response retain more of the source appearance, whereas regions with higher response allow larger changes. The image editor then completes the remaining sampling steps. Finally, projector compensation uses iterative refinement to obtain a compensation image for physical projection.

Figure 2: Scene response map construction. We evaluate the channelwise direct-response factor $B_{c}(\mathbf{u})$ . Channel averaging gives $R(\mathbf{u})$ , which is normalized over the reconstructed-object support $\Omega$ to form $M$ . The insets show representative regions with lower and higher response.

Figure 3: Response-guided sampling. At one sampling step, the Euler update produces $\widetilde{\mathbf{z}}_{i+1}$ , while $\mathbf{a}_{i+1}$ provides the source anchor at the same noise level. The scene response map, control strength, and temporal weight determine $w_{i}(\mathbf{u})$ , which moves the updated latent toward the source anchor.

Figure 4: Real-projection comparisons. Results are shown for an oil-painted-statue edit (top) and a painted-wood edit (bottom). The upper and lower rows show generated targets and camera-captured results, respectively. Red and blue boxes indicate enlarged regions for comparing local texture and color reproduction. The prompts for these representative comparisons and additional qualitative results are provided in the supplementary material.

Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	$\Delta E$ $\downarrow$	CLIP $\uparrow$	BLIP-ITM $\uparrow$	Time (s/image) $\downarrow$	VRAM (GiB) $\downarrow$
Nepmap [ 14 ]	19.1512	0.5962	0.3885	9.8198	29.79	59.72	8.70	7.68
LAPIG [ 13 ] $\dagger$	13.4033	0.3163	0.4189	17.4810	28.41	48.06	9.08	5.60
Native-LongCat [ 43 ]	14.7231	0.3721	0.3369	14.5425	30.56	91.66	58.50	29.73
SREdit-LongCat	20.6623	0.5432	0.2316	8.2552	32.44	92.62	59.10	29.73
Native-Qwen [ 58 ]	16.5086	0.3608	0.3571	13.7814	31.61	88.61	88.37	57.94
SREdit-Qwen	21.3113	0.5690	0.2559	8.1197	32.10	94.77	89.10	57.95

Table 1: Performance and efficiency under real projection. PSNR, SSIM, LPIPS, and

\Delta E

are computed between the intended appearance of each method and its camera-captured result. CLIP and BLIP-ITM measure agreement with the requested appearance. Results are averaged over 18 prompts from six setups. Time is the wall-clock time required to generate one image, and VRAM is the peak allocated GPU memory during image generation. We use bold and underline to highlight the best and second-best results, respectively.

Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	$\Delta E$ $\downarrow$	CLIP $\uparrow$	BLIP-ITM $\uparrow$
SREdit-Qwen	21.3113	0.5690	0.2559	8.1197	32.10	94.77
SREdit-LongCat	20.6623	0.5432	0.2316	8.2552	32.44	92.62
Uniform-Qwen	21.9647	0.6129	0.2398	7.4097	31.07	93.63
Uniform-LongCat	20.9145	0.5487	0.2286	7.9873	31.73	90.16

Table 4: Scene response map ablation. For each image editor, the results are averaged over the same 18 cases from six setups. We use

r=0.2,S=2.5

for Qwen and

r=0.2,S=3.0

for LongCat. We use bold and underline to highlight the best and second-best results, respectively.

Metric	All cases	Qwen	LongCat
Text alignment	65.4%	61.4%	69.4%
Fewer artifacts	72.8%	63.9%	81.7%
Realism	75.0%	68.1%	81.9%
No misalignment	96.5%/80.4%	96.4%/90.3%	96.7%/70.6%
Overall preference	72.4%	65.3%	79.4%

Table 2: User study. Pairwise perceptual evaluation with 20 participants over 36 cases. Values for text alignment, artifacts, realism, and overall preference are the percentages of judgments favoring SREdit over the corresponding Native version. For visible misalignment, entries report the percentages judged free of misalignment for SREdit/Native, respectively.

Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	$\Delta E$ $\downarrow$
Native-Qwen	18.5257	0.6675	0.1159	10.6841
SREdit-Qwen	21.7335	0.7358	0.0965	7.6941

Table 3: Reference-guided editing. Quantitative comparison over 12 cases from four physical setups. Metrics are computed between each intended appearance and its camera-captured result within the foreground object mask and averaged over all cases. The better result for each metric is shown in bold .

Figure 5: Reference-guided qualitative results. Appearance editing using Qwen. The first two columns show the reference and source scene. The remaining columns show the intended and camera-captured appearances produced by Native-Qwen and SREdit-Qwen. The top row uses Chinese opera face painting as the reference for makeup transfer, whereas the bottom row uses emerald as the reference material for material replacement. Marked regions are enlarged for local comparison.

Figure 6: Control-parameter ablation. All results are physical projector–camera captures. Top: complete $r$ -by- $S$ parameter grids for both image editors. Bottom: mean CLIP and LPIPS for SREdit-Qwen and SREdit-LongCat over 18 cases (six setups, three prompts each). Dots mark measured settings, contours show interpolated trends, and red stars mark the best measured setting in each plot.

Figure 7: Scene response map ablation. Camera-captured results under real projection. Uniform replaces the scene response map with its mean while retaining the paired $r$ and $S$ values. Insets show unedited patches that retain the source color under Uniform; SREdit shows fewer such patches.

Figure 8: Failure case. This result uses $r=0.2$ and $S=2.5$ . Although it remains registered with the physical object, parts of the requested wood texture are weakened or spatially uneven in the intended and camera-captured appearances.

Figure S1: Qualitative comparison for Case 1. SREdit uses $(r,S)=(0.2,2.5)$ for Qwen and $(0.2,3.0)$ for LongCat. The upper and lower rows show generated appearances and camera-captured projection results, respectively.

Figure S2: Parameter ablation for Case 1. Columns vary $S$ , while rows vary $r$ . The upper and lower matrices show generated and camera-captured results, respectively, and the bottom plots report LPIPS and CLIP over the same parameter grid.

Figure S3: Qualitative comparison for Case 2. SREdit uses $(r,S)=(0.2,2.5)$ for Qwen and $(0.2,3.0)$ for LongCat. The upper and lower rows show generated appearances and camera-captured projection results, respectively.

Figure S4: Parameter ablation for Case 2. Columns vary $S$ , while rows vary $r$ . The upper and lower matrices show generated and camera-captured results, respectively, and the bottom plots report LPIPS and CLIP over the same parameter grid.

Figure S5: Qualitative comparison for Case 3. SREdit uses $(r,S)=(0.2,2.5)$ for Qwen and $(0.2,3.0)$ for LongCat. The upper and lower rows show generated appearances and camera-captured projection results, respectively.

Figure S6: Parameter ablation for Case 3. Columns vary $S$ , while rows vary $r$ . The upper and lower matrices show generated and camera-captured results, respectively, and the bottom plots report LPIPS and CLIP over the same parameter grid.

Figure S7: Qualitative comparison for Case 4. SREdit uses $(r,S)=(0.2,2.5)$ for Qwen and $(0.2,3.0)$ for LongCat. The upper and lower rows show generated appearances and camera-captured projection results, respectively.

Figure S8: Parameter ablation for Case 4. Columns vary $S$ , while rows vary $r$ . The upper and lower matrices show generated and camera-captured results, respectively, and the bottom plots report LPIPS and CLIP over the same parameter grid.

Figure S9: Qualitative comparison for Case 5. SREdit uses $(r,S)=(0.2,2.5)$ for Qwen and $(0.2,3.0)$ for LongCat. The upper and lower rows show generated appearances and camera-captured projection results, respectively.

Figure S10: Parameter ablation for Case 5. Columns vary $S$ , while rows vary $r$ . The upper and lower matrices show generated and camera-captured results, respectively, and the bottom plots report LPIPS and CLIP over the same parameter grid.

Figure S11: Qualitative comparison for Case 6. SREdit uses $(r,S)=(0.2,2.5)$ for Qwen and $(0.2,3.0)$ for LongCat. The upper and lower rows show generated appearances and camera-captured projection results, respectively.

Figure S12: Parameter ablation for Case 6. Columns vary $S$ , while rows vary $r$ . The upper and lower matrices show generated and camera-captured results, respectively, and the bottom plots report LPIPS and CLIP over the same parameter grid.

Figure S13: Qualitative comparison for Case 7. SREdit uses $(r,S)=(0.2,2.5)$ for Qwen and $(0.2,3.0)$ for LongCat. The upper and lower rows show generated appearances and camera-captured projection results, respectively.

Figure S14: Parameter ablation for Case 7. Columns vary $S$ , while rows vary $r$ . The upper and lower matrices show generated and camera-captured results, respectively, and the bottom plots report LPIPS and CLIP over the same parameter grid.

Figure S15: Qualitative comparison for Case 8. SREdit uses $(r,S)=(0.2,2.5)$ for Qwen and $(0.2,3.0)$ for LongCat. The upper and lower rows show generated appearances and camera-captured projection results, respectively.

Figure S16: Parameter ablation for Case 8. Columns vary $S$ , while rows vary $r$ . The upper and lower matrices show generated and camera-captured results, respectively, and the bottom plots report LPIPS and CLIP over the same parameter grid.

Figure S17: Qualitative comparison for Case 9. SREdit uses $(r,S)=(0.2,2.5)$ for Qwen and $(0.2,3.0)$ for LongCat. The upper and lower rows show generated appearances and camera-captured projection results, respectively.

Figure S18: Parameter ablation for Case 9. Columns vary $S$ , while rows vary $r$ . The upper and lower matrices show generated and camera-captured results, respectively, and the bottom plots report LPIPS and CLIP over the same parameter grid.

Figure S19: Qualitative comparison for Case 10. SREdit uses $(r,S)=(0.2,2.5)$ for Qwen and $(0.2,3.0)$ for LongCat. The upper and lower rows show generated appearances and camera-captured projection results, respectively.

Figure S20: Parameter ablation for Case 10. Columns vary $S$ , while rows vary $r$ . The upper and lower matrices show generated and camera-captured results, respectively, and the bottom plots report LPIPS and CLIP over the same parameter grid.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Comparison	Methods	Prompt
Prompts used in the main paper
\cref fig:teaser(b), text-guided editing
Blue Porcelain	SREdit	Give the toy a blue-and-white porcelain-style doll appearance, with an antique ceramic glaze and classic cobalt-blue patterns, while preserving the original shape and silhouette.
Ink Wash	SREdit	Give the toy an ink-wash-style doll appearance, with pale monochrome tones and flowing ink gradients, while preserving the original shape and silhouette.
Painted Wood	SREdit	Give the toy a painted-wood doll appearance, with warm colors and bold hand-painted floral patterns, while preserving the original shape and silhouette.
Tropical Style	SREdit	Give the toy a tropical outfit-style doll appearance, with vibrant island colors and bold floral patterns, while preserving the original shape and silhouette.

Appendix

Table S1: Editing prompts. Prompts used in \cref fig:teaser and in the representative main-paper and supplementary comparisons.

Image Editing

Image editing changes selected visual content while preserving the parts of an image that should remain unchanged.

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