mlsanityData to 14 Oct 2026

DynaMesh: Dynamic 3D Texture Generation

arXiv 2610.05529 ↗First posted 4 Oct 2026cs.CVcited by 0

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Abstract (arXiv)

We present DynaMesh, a dynamic texture generation method for 3D meshes. Given a textureless shape and a text prompt describing an effect, our method produces an appearance that evolves while the object's geometry remains unchanged. Previous works on dynamic 3D content generation have focused on motion, where an object's geometry and position change while keeping its appearance the same. Methods on texture generation sit on the other side of the problem, painting appearance onto a shape as a fixed surface property and not as an evolving process. Neither addresses a visual effect that propagates on a 3D object. A natural route consists of two generators: a video model that shows the effect from a single view, and an image-to-3D generator that lifts each frame to 3D. However, the latter has no notion of time, so running it per video frame produces a sequence that flickers, loses effect details, and yields a different mesh at every video frame. Our method addresses these failures by conditioning a video model on a render of the mesh and the prompt to obtain a reference video, then running a frozen image-to-3D generator on the video with two changes. The conditioning of each frame is blended over a temporal window, and low-rank adapters are fit per shape to restore the lost details. The mesh is encoded once for the whole sequence, so geometry is constant by construction, and the output is a single mesh with a texture per frame. Applied to various objects and effects, DynaMesh substantially improves over recent video-to-4D and texturing methods, and can generalize its temporal effect to different shapes never seen during training. Our project page is at https://threedle.github.io/dynamesh/.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 46 of its 58 references that are in the map. First posted on arXiv 4 Oct 2026.

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46 of 58 references.

References · 46

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34 more
  1. 2023LRM: Large Reconstruction Model for Single Image to 3Dcited by 849
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  14. 20234D-fy: Text-to-4D Generation Using Hybrid Score Distillation Samplingcited by 185
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  29. 2023Mesh Neural Cellular Automatacited by 11
  30. 2025FlexPainter: Flexible and Multi-View Consistent Texture Generationcited by 8
  31. 2024Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion Modelscited by 3
  32. 20253D PixBrush: Image-Guided Local Texture Synthesiscited by 3
  33. 2025Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Modelscited by 2
  34. 2026MV2UV: Generating High-quality UV Texture Maps with Multiview Promptscited by 1

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