Texture Consolidation of Generated Multi-View Renderings
Hongyi Liu, Yifan Wang, Vladimir G. Kim, Misha Kazhdan
We present a system for consolidating a sparse set of multi-view renderings into a single coherent texture image, providing a framework for integrating generative models for image generation within the geometry-processing pipeline. Given a texture-atlased triangle mesh and a set of renderings with associated camera parameters, we generate the texture image by: (1) back-projecting the renderings onto the mesh to obtain texture images with normalized colors and confidences; (2) solving for flow-fields on the mesh that align the colors to each other, taking into account the associated confidences; and (3) fusing the registered colors into a single texture image using gradient-domain stitching, again incorporating confidences. Our approach complements existing techniques for multi-view image generation, leveraging mesh-based optical flow and texture-space stitching. In doing so it addresses the challenge that while modern image generation pipelines generate highly convincing renderings, they struggle to produce renderings that are consistent across views. Code is available at https://github.com/hliuw/TextureConsolidation.
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