3D texture generation is receiving increasing attention, as it enables the creation of realistic and aesthetic texture materials for untextured 3D meshes. However, existing 3D texture generation methods are limited to producing only a few types of non-emissive PBR materials (e.g., albedo, metallic maps and roughness maps), making them difficult to replicate highly popular styles, such as cyberpunk, failing to achieve effects like realistic LED emissions. To address this limitation, we propose a novel task, emission texture generation, which enables the synthesized 3D objects to faithfully reproduce the emission materials from input reference images. Our key contributions include: first, We construct the Objaverse-Emission dataset, the first dataset that contains 40k 3D assets with high-quality emission materials. Second, we propose EmissionGen, a novel baseline for the emission texture generation task. Third, we define detailed evaluation metrics for the emission texture generation task. Our results demonstrate significant potential for future industrial applications. Dataset will be available at https://github.com/yx345kw/EmissionGen.
Figures & tables
Figure 1. Gallery of our generated emission texture 3D assets.
Figure 2. Overview of the Objaverse-Emission dataset construction pipeline. Starting from Objaverse 1.0 and Objaverse-XL, we perform (1) coarse asset filtering, (2) emission-material verification, (3) multi-strength and multi-illumination rendering, (4) luminous-area–based emission strength selection, and (5) VLM-based single-object screening. The resulting Objaverse-Emission dataset contains 40K high-quality 3D assets with reliable emission materials.
Figure 3. Dataset statistics of Objaverse-Emission: (a) word cloud of metadata tags; (b) categories distribution.
Figure 4. Model architecture of EmissionGen. Following Hunyuan3D-2.1 Paint, we use a dual-branch architecture. The input to the reference branch and emission strength branch is a reference image. The input to the generation branch are concatenation of latent noise and multi-view normal maps and position maps of corresponding 3D mesh. The output of generation branch is multi-view albedo maps and emission maps. In each generation block, there are reference attention and multi-view attention block. For the alignment of albedo maps and emission maps, the reference attention of albedo branch is shared with emission branch.
Figure 5. Glare disturbance from emission materials. The left image shows the reference rendering, while the middle and right images depict the incorrectly generated albedo and emission maps. The strong glow emitted by the sword illuminates the adjacent lap region of the warrior, and this glare is mistakenly baked into both the albedo and emission maps during generation.
Figure 6. Multi-view renderings of a 3D asset under varying emission strengths and environmental illuminations. During emission-strength disentanglement training, reference images are randomly sampled across different viewpoints, lighting conditions, and emission intensities to encourage illumination-invariant material learning.
Table 1. Quantitative evaluation on texture realism and diversity.
Figure 7. Qualitative comparison of different models. The left most column are reference images. On the right are multi-view images rendered from the complete synthesized textured 3D meshes generated by different models. The results show that EmissionGen achieves superior results compared to other methods. Please zoom in for better visualization of the details.
Method
Dice coefficient ↑
EmissionGen
0.98856
MAGE-Emission
0.98721
MVPainter-Emission
0.98749
Table 2. Emission map accuracy.
Figure 8. With ESDT (Emission Strength Disentanglement Training), the glare caused by emission materials will not be baked into albedo maps, and the generated emission maps will be correct.
Method
PSNR ↑
SSIM ↑
LPIPS ↓
CLIP-FID ↓
FID ↓
CLIP-I ↑
w/ seg loss
15.36
0.912
0.086
23.491
134.3
0.824
w/o seg loss
14.43
0.891
0.088
24.245
139.1
0.810
Table 3. Emission-channel segmentation loss ablation study.