Automatically generating photorealistic and self-consistent appearances for untextured 3D models is a critical challenge in digital content creation. The advancement of large-scale video generation models offers a natural approach: directly synthesizing 360-degree turntable videos (TTVs), which can serve not only as high-quality dynamic previews but also as an intermediate representation to drive texture synthesis and neural rendering. However, existing general-purpose video diffusion models struggle to maintain strict geometric consistency and appearance stability across the full range of views, making their outputs ill-suited for high-quality 3D reconstruction. To this end, we introduce TAPESTRY, a framework for generating high-fidelity TTVs conditioned on explicit 3D geometry. We reframe the 3D appearance generation task as a geometry-conditioned video diffusion problem: given a 3D mesh, we first render and encode multi-modal geometric features to constrain the video generation process with pixel-level precision, thereby enabling the creation of high-quality and consistent TTVs. Building upon this, we also design a method for downstream reconstruction tasks from the TTV input, featuring a multi-stage pipeline with 3D-Aware Inpainting. By rotating the model and performing a context-aware secondary generation, this pipeline effectively completes self-occluded regions to achieve full surface coverage. The videos generated by TAPESTRY are not only high-quality dynamic previews but also serve as a reliable, 3D-aware intermediate representation that can be seamlessly back-projected into UV textures or used to supervise neural rendering methods like 3DGS. This enables the automated creation of production-ready, complete 3D assets from untextured meshes. Experimental results demonstrate that our method outperforms existing approaches in both video consistency and final reconstruction quality.
Figures & tables
Figure 1: We introduce TAPESTRY for high-fidelity 3D appearance generation by synthesizing a reconstructable Turntable Video through strong geometric conditioning. This highly consistent video then serves as a robust data source to create a final, high-quality asset.
Figure 2: An overview of the TAPESTRY architecture. (a) Geometry-guided video generation. Our method generates a 3D consistent Turntable Video by injecting multi-modal geometric conditions and reference context into a DiT-based video diffusion model. (b) Our progressive texturing pipeline. We iteratively generate TTVs from new, optimized viewpoints and fuse their projections via Texture Baking. Each pass is conditioned on previously generated textures to ensure global consistency, continuing until full coverage is achieved.
Figure 3: Qualitative results of TAPESTRY. Our method generates a consistent Turntable Video, from which a complete and high-fidelity textured asset is produced.
Figure 4: Qualitative comparison. As shown, the controllable video baselines suffer from significant appearance drift, the Janus problem. In contrast, our method maintains strong object identity and consistency throughout the rotation, effectively eliminating such artifacts.
Video Metrics
Method
PSNR (↑)
SSIM (↑)
LPIPS (↓)
FVD (↓)
Wan2.1-Fun (Depth)
23.48
0.904
0.084
277.9
Wan2.1-Fun (Canny)
24.32
0.916
0.074
235.0
Diffusion as Shader
23.21
0.906
0.090
295.7
Ours
25.79
0.924
0.066
189.9
Table 1: Quantitative comparison of generated Turntable Videos.
Texture Metrics
Method
FID (↓)
KID (↓)
CLIP-S (↑)
Time (↓)
Paint3D
43.47
36.63
0.8880
96s
HY3D-2.0
31.45
22.52
0.9165
33s
TEXGen
39.11
35.09
0.8906
15 s
MV-Adapter
30.68
25.73
0.9291
32s
Ours
26.90
16.21
0.9488
87s
Table 2: Quantitative comparison of generated textures. All metrics are evaluated on multi-view renderings.
Method
PSNR (↑)
SSIM (↑)
LPIPS (↓)
FVD (↓)
w.o normal
25.15
0.918
0.073
203.8
w.o position
25.66
0.921
0.069
191.1
Ours
25.79
0.924
0.066
189.9
Table 3: Quantitative ablation results on the Geometry Control Fusion module
Figure 6: The initial Iter.1 result has an incomplete texture due to self-occlusion. The w/o Inpainting baseline that generates a second TTV independently suffers from visible seams and color shifts.In contrast, our full pipeline with context-aware inpainting produces a seamless and globally consistent result.
Figure 7: Our consistent TTVs can supervise both traditional texturing and neural representations like 3DGS, with the latter better capturing volumetric details like fur.