JanusMesh: Fast and Zero-Shot 3D Visual Illusion Generation via Cross-Space Denoising
2026-06-18 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors present a new fast method to create 3D objects that look like completely different things from different angles. Their approach works in two steps: first, they carefully merge shapes using a special process to avoid seams and glitches; second, they add textures that change depending on the view. This method is much quicker than previous ones and makes clearer, more realistic illusions without needing training. The authors tested their approach and found it works better and faster than older techniques.
3D meshvisual illusionoptimizationCLIPSigned Distance Field (SDF)voxel spacetexture synthesisdiffusion modelmulti-viewsemantic alignment
Authors
Siang-Ling Zhang, Huai-Hsun Cheng, Tsung-Ju Yang, Yu-Lun Liu
Abstract
Creating 3D visual illusions, a single 3D mesh that reveals entirely different semantics from various viewing angles, is a fascinating but tough challenge. Existing optimization-based methods are slow and can produce oversaturated colors. In contrast, naive stitching approaches fail to produce geometrically coherent objects. This results in visible unnatural seams and semantic leaks. In this paper, we present a fast and training-free framework for generating text-driven 3D visual illusions. Our approach decouples the generation into two stages. First, we propose a cross-space dual-branch denoising process. This process dynamically decodes 3D latents into voxel space for CLIP-guided orientation alignment and Signed Distance Field (SDF) blending, which ensures seamless geometric fusion. Second, we introduce a view-conditioned texture synthesis module that projects and aggregates view-specific 2D diffusion priors onto the fused geometry. Extensive experiments demonstrate that our method generates highly realistic, dual-semantic 3D illusions in just 3-5 minutes. It significantly outperforms existing methods in geometric integrity, semantic recognizability, and efficiency. Project page: https://siang1105.github.io/JanusMesh.github.io/