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Paper · arXiv 2405.08054

Coin3D: Controllable and Interactive 3D Assets Generation with Proxy-Guided Conditioning

Wenqi Dong, Bangbang Yang, Lin Ma, Xiao Liu, Liyuan Cui, Hujun Bao, Yuewen Ma, Zhaopeng Cui

22 upvotesMay 13, 2024arXiv 预印本
AI 摘要

Coin3D is an interactive 3D modeling framework that uses a coarse geometry proxy to control 3D generation, supports seamless local editing, and provides responsive previews using diffusion models and various novel techniques.

generative techniques2D diffusion methodsmasked inpainting3D generationcontrollable and interactive 3D assets modelingCoin3Dcoarse geometry proxyinteractive generation workflow3D adapterproxy-bounded editing strategyprogressive volume cachevolume-SDSmesh reconstructioninteractive generationlocal editingresponsive preview

Abstract

As humans, we aspire to create media content that is both freely willed and readily controlled. Thanks to the prominent development of generative techniques, we now can easily utilize 2D diffusion methods to synthesize images controlled by raw sketch or designated human poses, and even progressively edit/regenerate local regions with masked inpainting. However, similar workflows in 3D modeling tasks are still unavailable due to the lack of controllability and efficiency in 3D generation. In this paper, we present a novel controllable and interactive 3D assets modeling framework, named Coin3D. Coin3D allows users to control the 3D generation using a coarse geometry proxy assembled from basic shapes, and introduces an interactive generation workflow to support seamless local part editing while delivering responsive 3D object previewing within a few seconds. To this end, we develop several techniques, including the 3D adapter that applies volumetric coarse shape control to the diffusion model, proxy-bounded editing strategy for precise part editing, progressive volume cache to support responsive preview, and volume-SDS to ensure consistent mesh reconstruction. Extensive experiments of interactive generation and editing on diverse shape proxies demonstrate that our method achieves superior controllability and flexibility in the 3D assets generation task.

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